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These technologies are crucial in handling large volumes of text quickly and efficiently, making them indispensable in modern translation workflows.\n\n> *For the latest insights and developments in AI-powered translation, explore our [AI-related blog articles](https:\u002F\u002Flocalazy.com\u002Ftags\u002Fai).*","ai-powered-translation",[1874,1875],{"id":43,"slug":1340},{"id":1876,"slug":1877},189,"neural-machine-translation",{"id":1879,"status":4,"owner":1880,"created_on":1881,"title":1882,"excerpt":1883,"content":1884,"slug":1885,"meta_title":53,"meta_description":53,"canonical":53,"related_terms":1886},297,"a5e46ee7-1f50-4f81-ae2d-68a664c76aa2","2026-05-12T07:09:25.000Z","AI agent","A software program that learns from the digital environment to complete tasks on its own.","AI agents, also known as digital intelligent agents, operate by interpreting inputs from digital environments, including user interactions and data streams. They make decisions using algorithms, logic, or machine learning models and act independently to complete tasks, often improving their performance through experience. These agents can simulate human-like capabilities, such as understanding language and recognizing patterns.​ System monitoring tools detect anomalies and take corrective actions automatically.​ \n\n#### 🤖 Characteristics of intelligent agents:\n\n* They operate without constant human supervision.\n* Improve through machine learning or feedback loops.\n* Can collect and interpret data from digital sources.\n* Work toward specific objectives, either preprogrammed or learned.\n* Interact with users or other systems, often through natural language.​\n* Capable of adapting to changes and responding to new information in real time.\n\n#### 🔍 How are AI agents used?\n\n* Virtual assistants like Siri, Alexa, and Google Assistant interpret user requests and perform actions. \n* Customer support bots automate ticketing processes and resolve common issues. \n* Recommendation systems customize content or product suggestions based on user behavior. \n* Platforms like GenSpark use a multi-agent system to complete complex tasks like image localization, research synthesis, and UI adaptation with minimal human input.\n* Agentic code tools like Devin or GitHub Copilot can plan, write, and even test software components independently.\n\nWe can expect agents to become more widespread across domains and reduce the amount of manual work we have to do daily. \n\n### 🌏 AI agents in localization and translation\n\nAI agents are being used in localization and translation to speed up translation work, reduce manual effort, and keep content consistent across different languages. Unlike traditional rule-based systems, these agents learn from user interactions and real-world data, making real-time decisions to ensure translations are accurate and contextually appropriate. \n\nThey're used to: \n\n* Detect new or updated content for translation.\n* Launch and manage translation workflows automatically.\n* Suggest context-aware translations based on learned patterns.\n* Reduce the workload of linguists by acting as first-pass translators or reviewers.","ai-agent",[1887,1888,1889,1892],{"id":1866,"slug":1872},{"id":1876,"slug":1877},{"id":1890,"slug":1891},269,"rule-based-machine-translation-rbmt",{"id":1893,"slug":1894},293,"natural-language-processing-nlp",{"id":1896,"status":4,"owner":1897,"created_on":1898,"title":1899,"excerpt":1900,"content":1901,"slug":1902,"meta_title":53,"meta_description":53,"canonical":53,"related_terms":1903},185,"ceebb166-1990-467a-a514-56e71dfcf7a4","2026-05-12T07:09:23.000Z","Large Language Model (LLM)","A neural network that has absorbed and analyzed vast amounts of data and can produce text based on them.","A Large Language Model (LLM) is nothing but a type of Artificial Intelligence (AI) designed to understand and generate human language. These models are trained on vast amounts of text data and utilize machine learning techniques to perform a variety of language-related tasks by synthesizing information and answering to queries.\n\nLLMs are essential in Natural Language Processing (NLP) projects as they help in automating tasks such as text generation, translation, summarization, and more. Each LLM typically represents a sophisticated algorithm capable of producing human-like text based on the patterns it has learned from the training data.\n\n## 🤖 Key points about LLMs: \n\n* Some of the most well-known LLMs include OpenAI's GPT-4, Google's Gemini, and Facebook's RoBERTa.\n* LLMs are used in chatbots, virtual assistants, content creation, and language translation tools.\n* The effectiveness of an LLM depends on the quality and diversity of the text data it has been trained on.\n* The performance of LLMs is measured by their ability to generate coherent, contextually relevant, and grammatically correct text among others.\n* The development and deployment of LLMs raise ethical concerns related to bias, misinformation, and the impact on employment.\n\nUnlike basic language processing tools, LLMs are supposed to understand context, generate nuanced responses, and perform a variety of complex language tasks. Technologies like [Human-Assisted Machine Translation (HAMT)](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fhuman-assisted-mt) often integrate LLMs to achieve higher quality and more natural results.\n\n> *Learn more about LLMs and what they are able to do in the translation realm [in our blog](https:\u002F\u002Flocalazy.com\u002Ftags\u002Fai\u002F \"https:\u002F\u002Flocalazy.com\u002Ftags\u002Fai\u002F\").*","llm",[1904,1905,1906,1909,1912],{"id":1866,"slug":1872},{"id":1893,"slug":1894},{"id":1907,"slug":1908},304,"language-modeling",{"id":1910,"slug":1911},439,"tiny-language-model-tlm",{"id":1913,"slug":1914},440,"small-language-model-slm",{"id":170,"status":4,"owner":1916,"created_on":1917,"title":1918,"excerpt":1919,"content":1920,"slug":1921,"meta_title":53,"meta_description":53,"canonical":53,"related_terms":1922},"2bac48a8-b362-482f-b574-3bc71dca4c5b","2022-03-17T12:22:55.000Z","Translation Management System (TMS)","A software for translation process automation.","TMS help increase production efficiency by eliminating, automating, and delegating both one-time and repetitive tasks during the translation process. Translation management systems help teams manage multilingual content, assign tasks, track progress, and maintain translation quality. TMS platforms are widely used in localization projects to handle large amounts of text efficiently. \n\nThey support features like translation memories, glossaries, and automated workflows. This makes the translation process faster and more consistent, especially for businesses that need content in multiple languages. [CAT tools](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fcat-tool) are a subtype of TMSs.\n\nTranslation management systems [like Localazy](https:\u002F\u002Flocalazy.com\u002Fterm\u002Ftranslation-management-system) usually consist of two parts. One takes care of workflow management and the second one aids with the translation process using different technologies and methods, such as:\n\n* [Machine translation](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fmachine-translation)\n* [Translation Memory](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Ftranslation-memory)\n\nThese systems are ideal for large-scale translation projects, as they provide a vast range of features. \n\n## ☝️ Top features of a TMS:\n\n* Centralizes translation tasks in one platform.\n* Assigns jobs to translators and tracks progress.\n* Uses a translation memory to avoid repeated translations.\n* Ensures consistency with built-in term bases.\n* Helps companies scale content across languages.\n\nWith a TMS, teams can work together smoothly to manage translations. It provides the tools needed to keep projects on track and ensures high-quality results every time. However, they can be pricey for single users or small teams who may benefit from them as well, especially for [quality assurance](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fqa-quality-assurance) processes.","translation-management-system-tms",[1923,1924,1926],{"id":62,"slug":1355},{"id":36,"slug":1925},"cat-tool",{"id":43,"slug":1340},"Literal translation quality is no longer the benchmark: context is. We put ChatGPT, Claude, DeepSeek, Qwen, and Kimi K2 to the test in two language pairs, and the results might surprise you.","\u003Cp>History likes to repeat itself, and after the first battle usually comes the second. So here we are, running the sequel to the\u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\"> Great LLM Translation War\u003C\u002Fa> released in 2025.\u003C\u002Fp>\u003Cp>Since then, the AI models have improved significantly, new players have emerged, some have lost their popularity, and some have gained a bit more. But the number of users and output quality are not the only metrics that shifted.\u003C\u002Fp>\u003Cp>The way people interact with LLMs has fundamentally changed: from single prompts fired into a chat window to \u003Cstrong>structured workflows built around context, consistency, and control\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>In 2025, you picked a model and prompted it. In 2026, you create translation workflows built around context, consistency, and quality control. This includes AI in-context tools in Translation Management Systems that refine QA, style, and terminology consistency, and \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fbuild-vs-buy-in-localization-you-can-build-things-but-should-you\">vibe-coded solutions\u003C\u002Fa> with an LLM layer designed to meet the burning business needs. \u003C\u002Fp>\u003Cp>The popularity of context-infusing features such as AI Projects or custom assistants offered by some LLMs has grown, too, which is reflected in OpenAI's\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fthe-state-of-enterprise-ai-2025-report\u002F\"> State of Enterprise AI\u003C\u002Fa> report, published in December 2025. It indicates that usage of structured workflows such as \u003Cstrong>Projects\u003C\u002Fstrong> and \u003Cstrong>Custom GPTs\u003C\u002Fstrong> increased 19× year-to-date, now accounting for 20% of all enterprise messages.\u003C\u002Fp>\u003Cblockquote>In 2025, you picked a model and prompted it. In 2026, you create translation workflows built around context, consistency, and quality control\u003C\u002Fblockquote>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.databricks.com\u002Fresources\u002Febook\u002Fstate-of-ai-agents\">Databricks’ 2026 State of AI Agents report\u003C\u002Fa> paints a similar picture: enterprises are moving from single chatbots to \u003Cstrong>multi-agent workflows, which grew 327% in less than four months\u003C\u002Fstrong>. This shows a rapid shift toward more complex agentic systems rather than single-turn chat use.\u003C\u002Fp>\u003Cp>Add to it easy set-up for connection with external apps, quick access to tools covering your most common tasks (e.g., with Claude Agent Skills), and options to work with files on your desktop (e.g., with Claude Cowork), and you’ll gain endless possibilities of customization and context enhancement, leading to better quality. And this quality is sometimes as good as human translation.\u003C\u002Fp>\u003Cp>But don’t take my word for it.\u003C\u002Fp>\u003Ch2 id=\"well-configured-model-greater-best-model\">👀 Well configured model > \"best\" model \u003Ca class=\"markdownit-header-anchor\" href=\"#well-configured-model-greater-best-model\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>The findings of the\u003Ca href=\"https:\u002F\u002Faclanthology.org\u002F2025.wmt-1.22.pdf\"> WMT25 General Machine Translation Shared Task\u003C\u002Fa> run across 30 language pairs on challenging texts from six different domains and three source languages indicate that \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Fmodels\u002Fgemini\u002F2-5-pro\">Gemini 2.5 Pro\u003C\u002Fa>\u003C\u002Fstrong> deserves the highest place on the podium. It was the top performer for 14 language pairs, and the translation quality generated by this model was equal to or better than human translation in 10 of those pairs.\u003C\u002Fp>\u003Cp>What’s more, human translations, often considered the “gold standard,” ended up in the top cluster for only six of the 15 language pairs. These results could reflect the stylistic or lexical preferences of annotators, but one thing is sure: \u003Cstrong>when properly set up and equipped with the right context\u003C\u002Fstrong>, AI models can now deliver surprisingly accurate output. \u003C\u002Fp>\u003Cblockquote>The best-performing models now are the best configured, the most context-aware, and the most consistent ones\u003C\u002Fblockquote>\u003Cp>Similar observations can be found in the \u003Ca href=\"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fintento-releases-9th-annual-state-of-translation-automation-2025-industry-report-302598803.html\">State of Translation Automation 2025 Industry Report\u003C\u002Fa> by Intento. Its data shows that tailored solutions consistently beat generic engines. What’s more, human reviewers frequently couldn't tell AI output apart from human translation and often preferred the text created by AI. Instead of naming a single best-performing model, \u003Cstrong>the report indicates that the best scenario is to use a multi-agent workflow\u003C\u002Fstrong>. With separate agents for translation, review, and post-editing, this solution produced the highest average performance, earning the top ratings in 9 of 11 language pairs. \u003C\u002Fp>\u003Cp>The results vary per language pair (e.g., Claude Opus 4 and Sonnet 3.7 ranked the best for German, Japanese, Korean, and Italian, while Gemini 2.5 Pro and Flash were the best for Chinese, Ukrainian, and French), but the key message to take home is: \u003Cstrong>the model choice matters less than how you configure it\u003C\u002Fstrong>. This confirms the prevailing assumption that once fueled by the right context, your AI model can be unstoppable.\u003C\u002Fp>\u003Ch2 id=\"the-battle-rules-explained\">🤺 The battle rules explained \u003Ca class=\"markdownit-header-anchor\" href=\"#the-battle-rules-explained\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>Considering how fast things are changing, a follow-up to the Great LLM Translation War seems like a must. So, I’ve risen to the challenge and divided the world into two parts again: \u003Cstrong>East and West, and assigned three models to each side\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>The East Side is represented by \u003Cstrong>Kimi K2\u003C\u002Fstrong>, \u003Cstrong>DeepSeek\u003C\u002Fstrong>, and\u003Cstrong> Qwen\u003C\u002Fstrong>, whereas the West Side is all about \u003Cstrong>ChatGPT\u003C\u002Fstrong>, \u003Cstrong>Claude\u003C\u002Fstrong>, and \u003Cstrong>Gemini\u003C\u002Fstrong>.\u003C\u002Fp>\u003Ch3 id=\"why-these-models\">Why these models? \u003Ca class=\"markdownit-header-anchor\" href=\"#why-these-models\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>The battle participants were partially selected by popularity scores (the West Side models), partially by curiosity (Kimi K2, the newest of them all), and partially to allow for handy comparison with the first battle, where DeepSeek and Qwen \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\">gained surprisingly good results\u003C\u002Fa>.\u003C\u002Fp>\u003Ch3 id=\"what-are-we-testing\">What are we testing? \u003Ca class=\"markdownit-header-anchor\" href=\"#what-are-we-testing\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>📄 The full text provided for each model\u003Cstrong> is more complex\u003C\u002Fstrong> than in battle no. 1; \u003Cstrong>it’s also longer\u003C\u002Fstrong>, comprising 450 words of marketing copy about a fictional AI-powered sleep-tracking ring. The source is English and \u003Cstrong>requires strict adherence to reference materials\u003C\u002Fstrong> to be rendered correctly. That’s why every model will receive not only the source, but also the relevant context.\u003C\u002Fp>\u003Cp>💬 Where possible, all these assets will be fed into a project built inside the model – no integrations with TMS or other platforms, just a standard setup on the LLM website with reference files attached to the prompt. This makes the battle fair for everyone (not all tested LLMs can be integrated with localization platforms), and you, my dear reader, can easily replicate the results without worrying about any missing technology or connections.\u003C\u002Fp>\u003Cp>For a better taste of the source, here’s a sample of our imaginary product:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable style=\"border: medium; border-collapse: collapse;\">\u003Ccolgroup>\u003Ccol width=\"602\">\u003C\u002Fcolgroup>\u003Ctbody>\u003Ctr style=\"height: 231.60144pt;\">\u003Ctd style=\"border-width: 0.681818pt; border-style: solid; border-color: rgb(0, 0, 0); vertical-align: top; padding: 0pt 5pt; overflow: hidden; overflow-wrap: break-word;\">\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> \u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Meet Lumi Ring. Your nights, finally understood.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">You spend a third of your life asleep. Lumi Ring spends it working.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Worn on your finger, powered by sensors you won't feel, Lumi Ring tracks what your body is quietly doing while you rest — and turns it into something you can actually use the next morning.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Precision that doesn't keep you up at night\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">At the core of every Lumi Ring is a clinical-grade \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">photoplethysmography (PPG) sensor\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> paired with a \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3-axis accelerometer\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, continuously monitoring your \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">heart rate variability (HRV)\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">blood oxygen saturation (SpO₂)\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, and \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">skin temperature deviation\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">. These aren't vanity metrics. They're the signals your body uses to tell you something — and most wearables have been getting the translation wrong.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> \u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Ch3 id=\"what-about-assessment\">What about assessment? \u003Ca class=\"markdownit-header-anchor\" href=\"#what-about-assessment\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>To allow for a direct comparison, the scoring criteria will remain the same. Output from every model will be assessed by professional translators based on:\u003C\u002Fp>\u003Cp>1️⃣ \u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003Cbr>2️⃣ \u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003Cbr>3️⃣ \u003Cstrong>Consistency with the style guide\u003C\u002Fstrong>\u003Cem>\u003Cstrong> \u003C\u002Fstrong>\u003C\u002Fem>(e.g., gender neutral, engaging)\u003Cbr>4️⃣ \u003Cstrong>Contextual appropriateness\u003C\u002Fstrong> (e.g., does it fit the marketing context? Is it right for the target users?)\u003Cbr>5️⃣ \u003Cstrong>Accuracy\u003C\u002Fstrong> (how closely the translated text matches the meaning of the source text)\u003Cbr>6️⃣ \u003Cstrong>Fluency\u003C\u002Fstrong> (whether the translated text reads naturally in the target language)\u003C\u002Fp>\u003Cp>All these indicators will be assessed \u003Cstrong>on a scale from 1 to 5 for two language pairs \u003C\u002Fstrong>covered in the Battle no. 1: \u003Cstrong>🇵🇱 English-Polish\u003C\u002Fstrong>, a medium-source language, and \u003Cstrong>🇭🇷 English-Croatian\u003C\u002Fstrong>, \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Flow-resource-languages\">a low-resource language\u003C\u002Fa>. A note on the last indicator: it replaces the “Correct rendering of mobile app strings” from the previous test, since now the translated content will differ. \u003C\u002Fp>\u003Cp>✍️ This time, the source text won’t contain mobile app strings, \u003Cstrong>but\u003C\u002Fstrong> \u003Cstrong>something more challenging: marketing copy for a technical product\u003C\u002Fstrong>\u003Cem>.\u003C\u002Fem> Packed with brand voice, technical terms, and references to other campaigns. Something where literal translation is a big no-no, style guide matters, and consistency with previously released content is crucial.\u003C\u002Fp>\u003Cp>📓 Each LLM will be asked to provide a context-specific translation, based on \u003Cstrong>a detailed style guide \u003C\u002Fstrong>(including, e.g., friendly, approachable style, gender-neutral language, correct terminology, non-translatable terms), \u003Cstrong>glossary\u003C\u002Fstrong> (10 terms in English with Polish and Croatian translations), and \u003Cstrong>translation memory uploaded as an XLSX file\u003C\u002Fstrong> (30 segments of varied match rate, from 100% to 50%). The instructions will also contain information about the type of results we’re looking for, such as natural, engaging translation consistent with the uploaded documents.\u003Cbr>\u003Cbr>🧠 For each LLM, we’ll choose the currently newest version, preferably with the thinking capability, since analyzing so many references requires a deeper understanding.\u003C\u002Fp>\u003Cp>💬 Finally, each model will be presented with the same \u003Cstrong>prompt\u003C\u002Fstrong>:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable style=\"border: medium; border-collapse: collapse;\">\n  \u003Ccolgroup>\n    \u003Ccol width=\"714\">\n  \u003C\u002Fcolgroup>\n  \u003Ctbody>\n    \u003Ctr>\n      \u003Ctd style=\"border-width: 0.681818pt; border-style: solid; border-color: rgb(0, 0, 0); vertical-align: top; padding: 0pt 5pt; overflow: hidden; overflow-wrap: break-word;\">\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Act as a professional English-to-{TARGET_LANGUAGE} translator specializing in marketing copy for consumer technology and wellness products. I will provide you with a source text, a marketing description for \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Lumi Ring\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, an AI-powered sleep tracking ring.\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">As reference materials, use these files uploaded as {Sources\u002FProject Files\u002Fattached below – depending on the LLM’s UI}:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cul style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A glossary\u003C\u002Fspan>\u003Cspan> (English–{TARGET_LANGUAGE}) with approved terminology\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A style guide\u003C\u002Fspan>\u003Cspan> with brand voice, register, and grammatical rules for {TARGET_LANGUAGE}\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A translation memory\u003C\u002Fspan>\u003Cspan> with previously approved English–{TARGET_LANGUAGE} segments and their match rates\u003C\u002Fspan>\n          \u003C\u002Fli>\n        \u003C\u002Ful>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Your task:\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> Translate the full source text into {TARGET_LANGUAGE} according to the rules below.\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Reference hierarchy (in order of priority):\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Col style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Translation memory\u003C\u002Fspan>\u003Cspan> — for any segment matching at 100%, reuse the target verbatim. For high fuzzy matches (85–95%), follow the existing translation pattern closely and adapt only what is necessary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Glossary\u003C\u002Fspan>\u003Cspan> — every term listed in the glossary must be translated using the approved form on first use. Acronyms are retained in English with the target-language expansion in parentheses on first use, per the glossary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Style guide\u003C\u002Fspan>\u003Cspan> — apply all brand voice, register, formatting, and grammatical rules. In case of conflict, the style guide overrides defaults but does not override TM or glossary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n        \u003C\u002Fol>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Translation requirements:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cul style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve the brand voice: friendly, approachable, confident, warm — never clinical or overly formal.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Use \u003Cspan style=\"font-weight: 700;\">gender-neutral language\u003C\u002Fspan> throughout, following the strategies defined in the style guide. Avoid masculine-default forms when addressing the reader.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Keep brand names \u003Cspan style=\"font-weight: 700;\">invariable\u003C\u002Fspan>: Lumi Ring, Lumi Band Pro, SleepIQ™, Lumi Coach. Do not decline them through grammatical cases.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve \u003Cspan style=\"font-weight: 700;\">sentence rhythm\u003C\u002Fspan>: do not merge short punchy sentences into longer ones for the sake of \"more natural\" target-language prose. Short sentences are intentional brand voice devices.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve \u003Cspan style=\"font-weight: 700;\">all formatting exactly\u003C\u002Fspan>: bold on technical terms, the ™ symbol, the SpO₂ subscript, em dashes, paragraph breaks, bullet points.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Translate idioms and brand taglines for \u003Cspan style=\"font-weight: 700;\">meaning, not word-for-word\u003C\u002Fspan>.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Avoid unnecessary anglicisms when a natural target-language equivalent is in the glossary. Do not leave English terms untranslated unless explicitly retained per the glossary.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Apply all target-language–specific rules from the style guide, including diacritics, address forms, voice (active\u002Fpassive) preferences, and case\u002Fagreement requirements.\u003C\u002Fli>\n        \u003C\u002Ful>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Output format:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Col style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">The full translation only — no commentary, no explanations.\u003C\u002Fli>\n        \u003C\u002Fol>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Do not summarize the source. Do not add disclaimers about translation difficulty. Translate.\u003C\u002Fspan>\n        \u003C\u002Fp>\n      \u003C\u002Ftd>\n    \u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Cblockquote>\u003Cem>\u003Cstrong>Disclaimer\u003C\u002Fstrong>: The translation quality assessments and comparisons presented in this article are based on a limited set of tests performed by language professionals and should not be considered exhaustive or definitive. Due to constraints such as access to full model capabilities, proprietary algorithms, and the broad range of potential test conditions, our benchmarking efforts may not fully capture the capabilities of each model. We encourage readers to consult additional research and papers that offer more extensive benchmarking analyses. Bear in mind also that models used in these tests are updated and change all the time. Our benchmarking criteria might be limited.\u003C\u002Fem>\u003C\u002Fblockquote>\u003Ch2 id=\"the-great-llm-battle-no-2\">⚔️ The great LLM battle no. 2 \u003Ca class=\"markdownit-header-anchor\" href=\"#the-great-llm-battle-no-2\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>Below you can see the result of our great translation battle, categorized by models. First come the three Western LLMs, followed by the Eastern group.\u003C\u002Fp>\u003Ch3 id=\"lessa-hrefhttpschatgptcomgreaterchatgptlessagreater-version-lessa-hrefhttpshelpopenaicomenarticles11909943-gpt-55-in-chatgptgreatergpt-55lessagreater-standard-thinking\">\u003Ca href=\"https:\u002F\u002Fchatgpt.com\">ChatGPT\u003C\u002Fa>, version \u003Ca href=\"https:\u002F\u002Fhelp.openai.com\u002Fen\u002Farticles\u002F11909943-gpt-55-in-chatgpt\">GPT-5.5\u003C\u002Fa>, standard thinking \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpschatgptcomgreaterchatgptlessagreater-version-lessa-hrefhttpshelpopenaicomenarticles11909943-gpt-55-in-chatgptgreatergpt-55lessagreater-standard-thinking\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>For this and the following model, translations were set up as \u003Cstrong>Projects\u003C\u002Fstrong>. In Polish, all glossary terms landed correctly, and the style guide was applied. On the TM side, the model tried to make an effort, but it was quite selective. For 95% matches, it often reached for its own phrasing instead, for example, it translated “while you rest” as \u003Cem>\"gdy odpoczywasz\"\u003C\u002Fem> rather than \u003Cem>\"podczas odpoczynku\"\u003C\u002Fem> available in the TM.\u003C\u002Fp>\u003Cp>It made similar blunders for \u003Cem>\"dane z czujników\"\u003C\u002Fem> instead of \u003Cem>\"dane czujnikowe\"\u003C\u002Fem> (“sensor data”) or \u003Cem>\"metryki próżności\"\u003C\u002Fem> instead of \u003Cem>\"wskaźniki próżności\"\u003C\u002Fem> (“vanity metrics”) that were expected based on the TM. Most 85% matches were missed entirely. There were three 100% matches available; the model applied two of them and rewrote the third from scratch. And that was the opening sentence – so not something hidden in the middle that could be difficult to recognize.\u003C\u002Fp>\u003Cp>It seems \u003Cstrong>ChatGPT trusted itself more than the abundant TM\u003C\u002Fstrong>. The translation was \u003Cstrong>accurate, but not entirely fluent\u003C\u002Fstrong>, with many awkward or slightly literal phrases.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png\" class=\"kg-image\" alt=\" English to Polish translation on ChatGPT, version GPT-5.5.\" loading=\"lazy\" width=\"934\" height=\"950\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png 934w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, this model provided the most balanced output overall. It ranked high in every single category, and\u003Cstrong> the output required only minor post-editing\u003C\u002Fstrong>, mainly for a few slightly literal formulations.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-ChatGPT.png\" class=\"kg-image\" alt=\" English to Croatian translation on ChatGPT, version GPT-5.5.\" loading=\"lazy\" width=\"1187\" height=\"742\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FHR-ChatGPT.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FHR-ChatGPT.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-ChatGPT.png 1187w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"lessa-hrefhttpswwwanthropiccomnewsclaude-opus-4-7greaterclaude-opus-47lessagreater\">\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-opus-4-7\">Claude Opus 4.7\u003C\u002Fa> \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpswwwanthropiccomnewsclaude-opus-4-7greaterclaude-opus-47lessagreater\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fclaude.ai\u002F\">Claude\u003C\u002Fa> turned out to be \u003Cstrong>the most fluent of the six for the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ftranslate\u002Fen-pl_pl\">Polish translation\u003C\u002Fa>\u003C\u002Fstrong>. The model applied all TM matches and missed only one 75% entry, rendering \u003Cem>\"Wskaźnik stresu autonomicznego\"\u003C\u002Fem> instead of the TM's \u003Cem>\"Autonomiczny wskaźnik stresu\"\u003C\u002Fem>. Short source sentences came out less clumsy than in any other model, with fewer word-for-word calques. One headline tripped it up: \u003Cem>\"Zbudowany na trzech latach Ciebie”\u003C\u002Fem> for “Built on three years of you” was too literal.\u003C\u002Fp>\u003Cp> Terminology and style guide were both applied correctly, hence the overall high rating.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FClaude-PL-translation.png\" class=\"kg-image\" alt=\"English to Polish translation in Claude Opus 4.7.\" loading=\"lazy\" width=\"808\" height=\"932\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FClaude-PL-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FClaude-PL-translation.png 808w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, the model provided strong results but struggled with style-guide compliance. The key issue was the \u003Cstrong>Croatian diacritics and incorrect SpO2 formatting\u003C\u002Fstrong> instead of SpO₂. Overall, the output for this model can be deemed usable for both languages; however, only after careful review.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-translation-in-Claude.png\" class=\"kg-image\" alt=\"English to Croatian translation in Claude Opus 4.7.\" loading=\"lazy\" width=\"1833\" height=\"821\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FHR-translation-in-Claude.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1833w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"lessa-hrefhttpsbloggoogleproducts-and-platformsproductsgeminigemini-3gemini-3-deep-thinkgreatergemini-3-thinkinglessagreater\">\u003Ca href=\"https:\u002F\u002Fblog.google\u002Fproducts-and-platforms\u002Fproducts\u002Fgemini\u002Fgemini-3\u002F#gemini-3-deep-think\">Gemini 3 Thinking\u003C\u002Fa> \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpsbloggoogleproducts-and-platformsproductsgeminigemini-3gemini-3-deep-thinkgreatergemini-3-thinkinglessagreater\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Since no project feature was available here, the files and instructions were uploaded together with the prompt. In Polish, \u003Ca href=\"https:\u002F\u002Fgemini.google.com\u002F\">the model\u003C\u002Fa> showed a couple of accuracy issues. \"Have been interpreting\" came out as \u003Cem>\"interpretowała\"\u003C\u002Fem> – a verb rendered in past tense, where \u003Cem>\"interpretuje\"\u003C\u002Fem> (present tense) would have been more appropriate in this context. A grammar slip appeared in one string, and the TM also took some hits. \u003Cstrong>The model failed to replicate a few phrases from the memory\u003C\u002Fstrong>, missing similar entries to ChatGPT. Terminology and style guide were otherwise applied correctly.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FPL-Gemini.png\" class=\"kg-image\" alt=\"English to Polish translation in Gemini 3 Thinking.\" loading=\"lazy\" width=\"911\" height=\"793\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FPL-Gemini.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FPL-Gemini.png 911w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, \u003Cstrong>the model ranked high for fluency but ignored the style guide on several occasions\u003C\u002Fstrong>. For example, the phrase \u003Cem>“fotopletizmografija (PPG) senzor”\u003C\u002Fem> wasn’t rendered correctly. A more idiomatic option would be \u003Cem>“senzor za fotopletizmografiju (PPG)”\u003C\u002Fem> or \u003Cem>“senzor fotopletizmografije (PPG)”\u003C\u002Fem>.  The term “SpO₂” wasn’t formatted as per the style guide, some phrases included agreement problems (e.g., in \u003Cem>“Dostupno sada u bojama Midnight, Chalk i Sand.”\u003C\u002Fem>), and a few technical terms came out with awkward phrasing.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FGemini-HR-translation.png\" class=\"kg-image\" alt=\"English to Croatian translation in Gemini 3 Thinking.\" loading=\"lazy\" width=\"1023\" height=\"763\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FGemini-HR-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FGemini-HR-translation.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FGemini-HR-translation.png 1023w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"lessa-hrefhttpsapi-docsdeepseekcomnewsnews250120greaterdeepseek-r1lessagreater-deepthinkexpert\">\u003Ca href=\"https:\u002F\u002Fapi-docs.deepseek.com\u002Fnews\u002Fnews250120\">DeepSeek R1\u003C\u002Fa> (DeepThink\u002FExpert) \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpsapi-docsdeepseekcomnewsnews250120greaterdeepseek-r1lessagreater-deepthinkexpert\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>There is no project feature here either, so files and instructions were uploaded together with the prompt. For both translation tasks, \u003Cstrong>the model took the longest time to think\u003C\u002Fstrong>.\u003Cstrong> \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ftranslate\u002Fen-hr_hr\">Croatian translation\u003C\u002Fa> appeared after approximately 6 minutes, while for other models, the thinking time never exceeded 3 minutes. Before displaying the Polish translation, \u003Ca href=\"https:\u002F\u002Fwww.deepseek.com\u002Fen\u002F\">the model\u003C\u002Fa> thought for 211 seconds. This time was not spent recklessly, though. \u003Cstrong>Deepseek turns out to be the most thorough on TM entries in both languages\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>For example, in Polish, all matches were applied correctly, including the low ones and the tricky entry with \u003Cem>\"autonomiczny wskaźnik stresu\"\u003C\u002Fem> (“autonomic stress index“) that tripped up most other models. \u003C\u002Fp>\u003Cp>In Croatian, the model applied the TM, style guide, and glossary with surgical precision, delivering the second-best translation.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png\" class=\"kg-image\" alt loading=\"lazy\" width=\"857\" height=\"937\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png 857w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>It was not all roses, though, and both languages included minor blunders. In Croatian, some phrases were \u003Cstrong>slightly less natural from the marketing perspective\u003C\u002Fstrong>. One good example of this issue can be found in \u003Cem>“na temelju vaše akumulacije duga spavanja u stvarnom vremenu”\u003C\u002Fem>. The phrase was translated accurately, but sounds nothing like the approachable technical language prescribed in the style guide. A lighter Croatian marketing version would be \u003Cem>“na temelju vašeg stvarnog nakupljanja duga spavanja”\u003C\u002Fem>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png\" class=\"kg-image\" alt=\"English to Croatian translation in DeepSeek R1.\" loading=\"lazy\" width=\"1030\" height=\"890\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 1030w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Polish, there were minor grammar mistakes, literal phrasing, and a few clumsy constructions. Gender neutrality was missed for \u003Cem>\"pracowników\" \u003C\u002Fem>(“workers”), but other requirements of the style guide and terminology were applied correctly.\u003C\u002Fp>\u003Cp>\u003Cstrong>It seems that what DeepSeek gained in precision, it lost in fluency\u003C\u002Fstrong>.\u003C\u002Fp>\u003Ch3 id=\"lessa-hrefhttpsqwenaiblogidqwen36greaterqwen-36-pluslessagreater\">\u003Ca href=\"https:\u002F\u002Fqwen.ai\u002Fblog?id=qwen3.6\">Qwen 3.6-Plus\u003C\u002Fa> \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpsqwenaiblogidqwen36greaterqwen-36-pluslessagreater\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fchat.qwen.ai\">This model\u003C\u002Fa> offers a Project feature, so reference files were uploaded as project files. It also took longer to analyze the file, compared to the Western group models. In Polish, the TM compliance was solid with only one low-match entry missed \u003Cem>(\"Autonomiczny wskaźnik stresu\")\u003C\u002Fem>. The problem was fluency. Clumsy compounds like \u003Cem>\"mikro-wybudzenia\"\u003C\u002Fem>, awkward phrase order in \u003Cem>\"faza lekkiego snu\"\u003C\u002Fem> for \u003Cem>“light sleep transitions\u003Cstrong>”\u003C\u002Fstrong>\u003C\u002Fem>, and constructions like \u003Cem>\"kontekst do działania\" (“Actionable context”)\u003C\u002Fem> and \u003Cem>\"stres późnonocny\"\u003C\u002Fem> \u003Cem>(“late-night stress “)\u003C\u002Fem> made the text feel unpolished. Passive voice appeared where it wasn't needed. \u003Cstrong>The model translated accurately, but not as a professional native translator would\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-PL.png\" class=\"kg-image\" alt=\"English to Polish translation in Qwen 3.6-Plus.\" loading=\"lazy\" width=\"1153\" height=\"888\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FQwen-PL.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FQwen-PL.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-PL.png 1153w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>Things didn’t look up for the Croatian translation either. Overall, \u003Cstrong>Qwen provided the weakest output for this language pair\u003C\u002Fstrong>. Grammar hiccups, case-related conflict, and missing context were the key issues behind the lowest ranking. One example of issues in this category is the phrase \u003Cem>“na temelju vašeg dug spavanja u stvarnom vremenu”\u003C\u002Fem>, which is not grammatically correct. After \u003Cem>“vašeg”\u003C\u002Fem>, the noun phrase must agree in case, so the correct form would be \u003Cem>“vašeg duga spavanja”\u003C\u002Fem> or, more naturally in this context, \u003Cem>“vašeg stvarnog nakupljanja duga spavanja”\u003C\u002Fem>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-HR-trans.png\" class=\"kg-image\" alt=\"English to Croatian translation in Qwen 3.6-Plus.\" loading=\"lazy\" width=\"1646\" height=\"827\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FQwen-HR-trans.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FQwen-HR-trans.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2026\u002F05\u002FQwen-HR-trans.png 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-HR-trans.png 1646w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"lessa-hrefhttpswwwkimicomai-modelskimi-k2-6greaterkimi-k26-thinkinglessagreater\">\u003Ca href=\"https:\u002F\u002Fwww.kimi.com\u002Fai-models\u002Fkimi-k2-6\">Kimi K2.6 Thinking\u003C\u002Fa> \u003Ca class=\"markdownit-header-anchor\" href=\"#lessa-hrefhttpswwwkimicomai-modelskimi-k2-6greaterkimi-k26-thinkinglessagreater\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Like DeepSeek, \u003Ca href=\"https:\u002F\u002Fwww.kimi.com\">this model\u003C\u002Fa> spent a lot of time on thinking (nearly 9 minutes), and all instructions plus files had to be fed as a simple prompt because of the \u003Cstrong>missing Projects feature\u003C\u002Fstrong>. In the case of the Polish translation, the thinking time was rewarded with \u003Cstrong>probably the most creative translation in the set\u003C\u002Fstrong>. It was less literal than ChatGPT or Gemini, with noticeably better flow. On TM, Kimi missed only one low-match entry \u003Cem>(\"wskaźniki stresu autonomicznego\")\u003C\u002Fem>. Two issues pulled the score down. The first one was the gendered forms for \u003Cem>\"podróżników\"\u003C\u002Fem> and \u003Cem>\"pracowników\"\u003C\u002Fem>, although the style guide required neutral phrasing. Another one was a fluency glitch in the final two sentences, both of which contained the same word \u003Cem>(\"już\")\u003C\u002Fem>, a repetition that a quick read-through would have caught. Or one that a professional translator wouldn’t have made at all.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-PL.png\" class=\"kg-image\" alt=\"English to Polish translation in Kimi K2.6 Thinking.\" loading=\"lazy\" width=\"908\" height=\"935\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FKimi-PL.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-PL.png 908w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>For the Croatian translation, the model generated natural and marketing-friendly text, doing its best to apply all the provided resources. The TM and glossary compliance were strong, but \u003Cstrong>it struggled with fluency\u003C\u002Fstrong>. There were linguistic and structural issues, including case\u002Fagreement problems and changes to line breaks.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-HR.png\" class=\"kg-image\" alt=\"English to Croatian translation in Kimi K2.6 Thinking.\" loading=\"lazy\" width=\"827\" height=\"895\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FKimi-HR.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-HR.png 827w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch2 id=\"whats-the-llm-leaderboard\">📋 What’s the LLM leaderboard? \u003Ca class=\"markdownit-header-anchor\" href=\"#whats-the-llm-leaderboard\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>This is how each LLM scored for each indicator and language combination:\u003C\u002Fp>\u003C!--kg-card-begin: markdown-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth style=\"text-align:center\">\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with  translation  memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with the  style guide\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Contextual  appropriateness\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Claude\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">28\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Kimi\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">DeepSeek\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Qwen\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Gemini\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">ChatGPT\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C!--kg-card-end: markdown-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 1\u003C\u002Fstrong>. LLM's scores for the Polish translation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C!--kg-card-begin: markdown-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth style=\"text-align:center\">\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with  translation  memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with the  style guide\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Contextual  appropriateness\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">ChatGPT\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">DeepSeek\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Claude\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">23\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Qwen\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">21\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Gemini\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Kimi\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C!--kg-card-end: markdown-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 2\u003C\u002Fstrong>. LLM's scores for the Croatian translation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cp>\u003Cstrong>History repeats itself. battle no. 1 ended with no clear winner, and so does battle no. 2\u003C\u002Fstrong>. All tested LLMs turned out to be extremely powerful and capable of rendering correct translations in Polish and Croatian, based on the provided context. No matter the setup (whether it was a project or a simple prompt with all files attached), the models accessed style guides, glossaries, and translation memories, spent some time analyzing the context, followed the prompt accurately, and created content that was not only linguistically correct, but also, in most cases, in line with the brand rules.\u003C\u002Fp>\u003Cp>\u003Cstrong>ChatGPT was the least accurate\u003C\u002Fstrong> when it comes to reusing entries from the Translation Memory, while \u003Cstrong>DeepSeek was the most precise\u003C\u002Fstrong> in this aspect, applying all matches, even those below 80%. Glossary and style guide enforcement (excluding the rule on gender neutrality) was handled correctly by most models in both languages.\u003C\u002Fp>\u003Cblockquote>This time, all tested LLMs were extremely powerful and capable of rendering correct translations in both Polish and Croatian when given the context. Guides, glossaries and TMs were used, and the content created based on them was mostly in line with brand rules\u003C\u002Fblockquote>\u003Cp>Of course, this doesn't mean that the translations were perfect. While there were no big issues in terms of accuracy, \u003Cstrong>fluency varied, leaving room for improvement\u003C\u002Fstrong>. Literal phrases and grammatically awkward constructions appeared in nearly every output to a different extent. Although the texts could have been infused with more creativity and personality, they were mostly on brand and aligned with the provided context.\u003C\u002Fp>\u003Ch2 id=\"east-or-west-whats-the-best\">🆚 East or West: what’s the best? \u003Ca class=\"markdownit-header-anchor\" href=\"#east-or-west-whats-the-best\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>When it comes to the Western vs. Eastern divide, our test shows that \u003Cstrong>DeepSeek, Qwen, and Kimi generate results comparable with the frontier models\u003C\u002Fstrong>, at least for the languages and content selected for our experiment.\u003C\u002Fp>\u003Cp>There is no clear winner in this group. The interesting part, however, is that \u003Cstrong>for Polish translation, the Eastern models slightly outperformed ChatGPT and Gemini\u003C\u002Fstrong>, \u003Cstrong>while Claude led\u003C\u002Fstrong> the overall leaderboard. \u003Cstrong>For Croatian, it was ChatGPT that took the spot no. 1\u003C\u002Fstrong>, followed by DeepSeek and Claude.\u003C\u002Fp>\u003Cp>Simply put, the East retains its strong position thanks to DeepSeek, and the West shines through strong results from \u003Cstrong>ChatGPT (for Croatian)\u003C\u002Fstrong> and from \u003Cstrong>Claude (for Polish)\u003C\u002Fstrong>. These results may, of course, vary depending on the language pairs, content type, and the quality of the provided context.\u003C\u002Fp>\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-yellow\">\u003Cdiv class=\"kg-callout-emoji\">👉\u003C\u002Fdiv>\u003Cdiv class=\"kg-callout-text\">Compared to our first LLM Translation War released in 2025, in the case of the Polish translation, DeepSeek and Qwen have retained their strong position, while Claude gained in strength, coming from place no. 5 to no. 1. Claude won the ranking for Croatian in 2025; now this model dropped to spot no. 3, while ChatGPT moved from no. 4 to no. 1.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Ch2 id=\"and-the-winner-is\">🏆 And the winner is… \u003Ca class=\"markdownit-header-anchor\" href=\"#and-the-winner-is\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>What does it all mean for your content? Which LLM should you choose for your translations?\u003C\u002Fp>\u003Cp>There’s no universal solution, but one thing is sure: the future is integrated. No one will spend time and tokens to manually run multiple prompts for multiple language pairs in several LLMs, and with several reference documents attached to the prompt. \u003Cstrong>For the best result, you need a system that lets you easily choose the model that delivers the best result for your language and content type\u003C\u002Fstrong>, upload context that stays in the system and is reusable, when necessary, plus provide instruction that can be applied to other projects. That’s exactly what an orchestrated, agentic system offers.\u003C\u002Fp>\u003Cp>One such solution is Localazy: you upload your glossary, add TM and a style guide, and \u003Cstrong>run pre-translation using their proprietary model, \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Flocalazy-ai\">Localazy AI\u003C\u002Fa>\u003C\u002Fstrong>, which has been fine-tuned to provide the best for localization use cases. The same context materials can be used for translation into several languages, so there’s no need to create a separate project per language or copy-paste your prompt in the LLM’s interface several times. \u003Cstrong>Everything is connected, easy to use, and transparent when it comes to token use\u003C\u002Fstrong>. \u003C\u002Fp>\u003Cp>If that sounds too good to be true, have a look at the test I’ve performed recently to find out if \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Flocalazy-ai-put-to-the-test-across-three-content-types\">Localazy AI can handle the context correctly\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2 id=\"faqs\">💬 FAQs \u003Ca class=\"markdownit-header-anchor\" href=\"#faqs\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Ch3 id=\"have-chinese-llms-improved-since-2025\">Have Chinese LLMs improved since 2025? \u003Ca class=\"markdownit-header-anchor\" href=\"#have-chinese-llms-improved-since-2025\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>According to\u003Ca href=\"https:\u002F\u002Fwww.scmp.com\u002Ftech\u002Ftech-trends\u002Farticle\u002F3335602\u002Fchinas-open-source-models-make-30-global-ai-usage-led-qwen-and-deepseek\"> research from OpenRouter and Andreessen Horowitz\u003C\u002Fa>, Chinese open-source models went from roughly 1.2% of global AI usage in late 2024 to nearly 30% by the end of 2025. Their performance has improved significantly. For example, \u003Ca href=\"https:\u002F\u002Fwww.e2enetworks.com\u002Fblog\u002Fdeepseek-v3-2-open-source-reasoning\">DeepSeek’s V3.2-Speciale model won gold medals\u003C\u002Fa> in the International Mathematical Olympiad and the International Olympiad in Informatics. \u003C\u002Fp>\u003Cp>According to\u003Ca href=\"https:\u002F\u002Fbenchlm.ai\u002Fblog\u002Fposts\u002Fbest-chinese-llm\"> BenchLM.ai\u003C\u002Fa>, the gap between the Chinese model and the global frontier is shrinking. Number one Chinese LLM is DeepSeek V4 Pro (Max), followed by DeepSeek V4 Pro (High) and Kimi K2.6. \u003Ca href=\"https:\u002F\u002Fwww.nist.gov\u002Fnews-events\u002Fnews\u002F2026\u002F05\u002Fcaisi-evaluation-deepseek-v4-pro\">CAISI Evaluation\u003C\u002Fa> assessed that DeepSeek V4’s capabilities lag behind the frontier models by about 8 months. This assessment also confirms that DeepSeek V4 is more cost-efficient than other models of similar capability.\u003C\u002Fp>\u003Ch3 id=\"are-chinese-ai-models-as-good-as-western-models-for-translation\">Are Chinese AI models as good as Western models for translation? \u003Ca class=\"markdownit-header-anchor\" href=\"#are-chinese-ai-models-as-good-as-western-models-for-translation\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Our tests prove that the top Chinese models (especially DeepSeek) are comparable to the Western models when it comes to translation. With the relevant context and precise instructions, these models are able to generate decent results.\u003C\u002Fp>\u003Ch3 id=\"which-llm-is-best-for-localizing-a-mobile-app\">Which LLM is best for localizing a mobile app? \u003Ca class=\"markdownit-header-anchor\" href=\"#which-llm-is-best-for-localizing-a-mobile-app\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>There’s no one-size-fits-all solution. The results will vary depending on the language. According to the findings of the \u003Ca href=\"https:\u002F\u002Faclanthology.org\u002Fanthology-files\u002Fpdf\u002Fwmt.real\u002F2025.wmt-1.1.pdf\">WMT25 General Machine Translation Shared Task\u003C\u002Fa>, the best system overall was Gemini 2.5 Pro (it ended up in the top cluster in 14 language pairs). Our experiments from the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\">Translation War no. 1 \u003C\u002Fa> focused on mobile apps showed that DeepSeek is the top model for localizing mobile apps into Polish, and Claude for Croatian.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fintento-releases-9th-annual-state-of-translation-automation-2025-industry-report-302598803.html\">The Intento report\u003C\u002Fa> highlights that it’s the multi-agent workflow that delivers the best results. Bear in mind that top models constantly get new updates, and their results change. What’s the best varies depending on whether you prioritize translation accuracy, brand voice, terminology consistency, creativity, or privacy constraints. Whatever you choose, don’t forget about the human review,\u003Ca href=\"https:\u002F\u002Finternationalachieversgroup.com\u002Flocalisation\u002Fmetrics-for-evaluating-machine-translation-using-llms\u002F\"> as studies show tone, nuance, and cultural fit still need careful oversight\u003C\u002Fa>.\u003C\u002Fp>","LLM translation war, pt. 2: Context beats model choice","Literal translation quality is no longer the benchmark: context is. We test ChatGPT, Claude, Gemini, DeepSeek, Qwen, and Kimi K2 to prove it.",{"id":205,"status":4,"created_on":1932,"modified_on":1933,"icon":1934,"header":1935,"description":1936,"button_label":1937,"link":1938},"2025-11-03T17:08:39.000Z","2026-06-02T14:07:21.000Z","control","Keep translations on-brand with Localazy AI","Make your product sound authentic in other languages in seconds! Upload your content and get AI translations that match your style, brand voice and terminology.","Try Localazy AI","features\u002Flocalazy-ai",{"slug":1822,"id":1815,"uuid":1940,"title":1821,"html":1941,"comment_id":1815,"feature_image":1825,"featured":94,"visibility":1942,"email_recipient_filter":1943,"created_at":1944,"updated_at":1859,"published_at":1823,"custom_excerpt":1927,"codeinjection_head":8,"codeinjection_foot":8,"custom_template":8,"canonical_url":8,"authors":1945,"tags":1951,"primary_author":1997,"primary_tag":1998,"url":1999,"excerpt":1927,"reading_time":927,"access":94,"send_email_when_published":93,"og_image":8,"og_title":8,"og_description":8,"twitter_image":8,"twitter_title":8,"twitter_description":8,"meta_title":1929,"meta_description":1930,"email_subject":8,"frontmatter":8,"dictionary":1864,"cta":1931,"plainTags":1860},"c3ce0349-b5d0-4795-9949-97ebafc7ffa7","\u003Cp>History likes to repeat itself, and after the first battle usually comes the second. So here we are, running the sequel to the\u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\"> Great LLM Translation War\u003C\u002Fa> released in 2025.\u003C\u002Fp>\u003Cp>Since then, the AI models have improved significantly, new players have emerged, some have lost their popularity, and some have gained a bit more. But the number of users and output quality are not the only metrics that shifted.\u003C\u002Fp>\u003Cp>The way people interact with LLMs has fundamentally changed: from single prompts fired into a chat window to \u003Cstrong>structured workflows built around context, consistency, and control\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>In 2025, you picked a model and prompted it. In 2026, you create translation workflows built around context, consistency, and quality control. This includes AI in-context tools in Translation Management Systems that refine QA, style, and terminology consistency, and \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fbuild-vs-buy-in-localization-you-can-build-things-but-should-you\">vibe-coded solutions\u003C\u002Fa> with an LLM layer designed to meet the burning business needs. \u003C\u002Fp>\u003Cp>The popularity of context-infusing features such as AI Projects or custom assistants offered by some LLMs has grown, too, which is reflected in OpenAI's\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fthe-state-of-enterprise-ai-2025-report\u002F\"> State of Enterprise AI\u003C\u002Fa> report, published in December 2025. It indicates that usage of structured workflows such as \u003Cstrong>Projects\u003C\u002Fstrong> and \u003Cstrong>Custom GPTs\u003C\u002Fstrong> increased 19× year-to-date, now accounting for 20% of all enterprise messages.\u003C\u002Fp>\u003Cblockquote>In 2025, you picked a model and prompted it. In 2026, you create translation workflows built around context, consistency, and quality control\u003C\u002Fblockquote>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.databricks.com\u002Fresources\u002Febook\u002Fstate-of-ai-agents\">Databricks’ 2026 State of AI Agents report\u003C\u002Fa> paints a similar picture: enterprises are moving from single chatbots to \u003Cstrong>multi-agent workflows, which grew 327% in less than four months\u003C\u002Fstrong>. This shows a rapid shift toward more complex agentic systems rather than single-turn chat use.\u003C\u002Fp>\u003Cp>Add to it easy set-up for connection with external apps, quick access to tools covering your most common tasks (e.g., with Claude Agent Skills), and options to work with files on your desktop (e.g., with Claude Cowork), and you’ll gain endless possibilities of customization and context enhancement, leading to better quality. And this quality is sometimes as good as human translation.\u003C\u002Fp>\u003Cp>But don’t take my word for it.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%91%80-well-configured-model-best-model\">👀 Well configured model > \"best\" model\u003C\u002Fh2>\u003Cp>The findings of the\u003Ca href=\"https:\u002F\u002Faclanthology.org\u002F2025.wmt-1.22.pdf\"> WMT25 General Machine Translation Shared Task\u003C\u002Fa> run across 30 language pairs on challenging texts from six different domains and three source languages indicate that \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Fmodels\u002Fgemini\u002F2-5-pro\">Gemini 2.5 Pro\u003C\u002Fa>\u003C\u002Fstrong> deserves the highest place on the podium. It was the top performer for 14 language pairs, and the translation quality generated by this model was equal to or better than human translation in 10 of those pairs.\u003C\u002Fp>\u003Cp>What’s more, human translations, often considered the “gold standard,” ended up in the top cluster for only six of the 15 language pairs. These results could reflect the stylistic or lexical preferences of annotators, but one thing is sure: \u003Cstrong>when properly set up and equipped with the right context\u003C\u002Fstrong>, AI models can now deliver surprisingly accurate output. \u003C\u002Fp>\u003Cblockquote>The best-performing models now are the best configured, the most context-aware, and the most consistent ones\u003C\u002Fblockquote>\u003Cp>Similar observations can be found in the \u003Ca href=\"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fintento-releases-9th-annual-state-of-translation-automation-2025-industry-report-302598803.html\">State of Translation Automation 2025 Industry Report\u003C\u002Fa> by Intento. Its data shows that tailored solutions consistently beat generic engines. What’s more, human reviewers frequently couldn't tell AI output apart from human translation and often preferred the text created by AI. Instead of naming a single best-performing model, \u003Cstrong>the report indicates that the best scenario is to use a multi-agent workflow\u003C\u002Fstrong>. With separate agents for translation, review, and post-editing, this solution produced the highest average performance, earning the top ratings in 9 of 11 language pairs. \u003C\u002Fp>\u003Cp>The results vary per language pair (e.g., Claude Opus 4 and Sonnet 3.7 ranked the best for German, Japanese, Korean, and Italian, while Gemini 2.5 Pro and Flash were the best for Chinese, Ukrainian, and French), but the key message to take home is: \u003Cstrong>the model choice matters less than how you configure it\u003C\u002Fstrong>. This confirms the prevailing assumption that once fueled by the right context, your AI model can be unstoppable.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%A4%BA-the-battle-rules-explained\">🤺 The battle rules explained\u003C\u002Fh2>\u003Cp>Considering how fast things are changing, a follow-up to the Great LLM Translation War seems like a must. So, I’ve risen to the challenge and divided the world into two parts again: \u003Cstrong>East and West, and assigned three models to each side\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>The East Side is represented by \u003Cstrong>Kimi K2\u003C\u002Fstrong>, \u003Cstrong>DeepSeek\u003C\u002Fstrong>, and\u003Cstrong> Qwen\u003C\u002Fstrong>, whereas the West Side is all about \u003Cstrong>ChatGPT\u003C\u002Fstrong>, \u003Cstrong>Claude\u003C\u002Fstrong>, and \u003Cstrong>Gemini\u003C\u002Fstrong>.\u003C\u002Fp>\u003Ch3 id=\"why-these-models\">Why these models?\u003C\u002Fh3>\u003Cp>The battle participants were partially selected by popularity scores (the West Side models), partially by curiosity (Kimi K2, the newest of them all), and partially to allow for handy comparison with the first battle, where DeepSeek and Qwen \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\">gained surprisingly good results\u003C\u002Fa>.\u003C\u002Fp>\u003Ch3 id=\"what-are-we-testing\">What are we testing?\u003C\u002Fh3>\u003Cp>📄 The full text provided for each model\u003Cstrong> is more complex\u003C\u002Fstrong> than in battle no. 1; \u003Cstrong>it’s also longer\u003C\u002Fstrong>, comprising 450 words of marketing copy about a fictional AI-powered sleep-tracking ring. The source is English and \u003Cstrong>requires strict adherence to reference materials\u003C\u002Fstrong> to be rendered correctly. That’s why every model will receive not only the source, but also the relevant context.\u003C\u002Fp>\u003Cp>💬 Where possible, all these assets will be fed into a project built inside the model – no integrations with TMS or other platforms, just a standard setup on the LLM website with reference files attached to the prompt. This makes the battle fair for everyone (not all tested LLMs can be integrated with localization platforms), and you, my dear reader, can easily replicate the results without worrying about any missing technology or connections.\u003C\u002Fp>\u003Cp>For a better taste of the source, here’s a sample of our imaginary product:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable style=\"border: medium; border-collapse: collapse;\">\u003Ccolgroup>\u003Ccol width=\"602\">\u003C\u002Fcolgroup>\u003Ctbody>\u003Ctr style=\"height: 231.60144pt;\">\u003Ctd style=\"border-width: 0.681818pt; border-style: solid; border-color: rgb(0, 0, 0); vertical-align: top; padding: 0pt 5pt; overflow: hidden; overflow-wrap: break-word;\">\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> \u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Meet Lumi Ring. Your nights, finally understood.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">You spend a third of your life asleep. Lumi Ring spends it working.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Worn on your finger, powered by sensors you won't feel, Lumi Ring tracks what your body is quietly doing while you rest — and turns it into something you can actually use the next morning.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Precision that doesn't keep you up at night\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-left: 7.086614pt; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">At the core of every Lumi Ring is a clinical-grade \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">photoplethysmography (PPG) sensor\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> paired with a \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3-axis accelerometer\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, continuously monitoring your \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">heart rate variability (HRV)\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">blood oxygen saturation (SpO₂)\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, and \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">skin temperature deviation\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">. These aren't vanity metrics. They're the signals your body uses to tell you something — and most wearables have been getting the translation wrong.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> \u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Ch3 id=\"what-about-assessment\">What about assessment?\u003C\u002Fh3>\u003Cp>To allow for a direct comparison, the scoring criteria will remain the same. Output from every model will be assessed by professional translators based on:\u003C\u002Fp>\u003Cp>1️⃣ \u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003Cbr>2️⃣ \u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003Cbr>3️⃣ \u003Cstrong>Consistency with the style guide\u003C\u002Fstrong>\u003Cem>\u003Cstrong> \u003C\u002Fstrong>\u003C\u002Fem>(e.g., gender neutral, engaging)\u003Cbr>4️⃣ \u003Cstrong>Contextual appropriateness\u003C\u002Fstrong> (e.g., does it fit the marketing context? Is it right for the target users?)\u003Cbr>5️⃣ \u003Cstrong>Accuracy\u003C\u002Fstrong> (how closely the translated text matches the meaning of the source text)\u003Cbr>6️⃣ \u003Cstrong>Fluency\u003C\u002Fstrong> (whether the translated text reads naturally in the target language)\u003C\u002Fp>\u003Cp>All these indicators will be assessed \u003Cstrong>on a scale from 1 to 5 for two language pairs \u003C\u002Fstrong>covered in the Battle no. 1: \u003Cstrong>🇵🇱 English-Polish\u003C\u002Fstrong>, a medium-source language, and \u003Cstrong>🇭🇷 English-Croatian\u003C\u002Fstrong>, \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Flow-resource-languages\">a low-resource language\u003C\u002Fa>. A note on the last indicator: it replaces the “Correct rendering of mobile app strings” from the previous test, since now the translated content will differ. \u003C\u002Fp>\u003Cp>✍️ This time, the source text won’t contain mobile app strings, \u003Cstrong>but\u003C\u002Fstrong> \u003Cstrong>something more challenging: marketing copy for a technical product\u003C\u002Fstrong>\u003Cem>.\u003C\u002Fem> Packed with brand voice, technical terms, and references to other campaigns. Something where literal translation is a big no-no, style guide matters, and consistency with previously released content is crucial.\u003C\u002Fp>\u003Cp>📓 Each LLM will be asked to provide a context-specific translation, based on \u003Cstrong>a detailed style guide \u003C\u002Fstrong>(including, e.g., friendly, approachable style, gender-neutral language, correct terminology, non-translatable terms), \u003Cstrong>glossary\u003C\u002Fstrong> (10 terms in English with Polish and Croatian translations), and \u003Cstrong>translation memory uploaded as an XLSX file\u003C\u002Fstrong> (30 segments of varied match rate, from 100% to 50%). The instructions will also contain information about the type of results we’re looking for, such as natural, engaging translation consistent with the uploaded documents.\u003Cbr>\u003Cbr>🧠 For each LLM, we’ll choose the currently newest version, preferably with the thinking capability, since analyzing so many references requires a deeper understanding.\u003C\u002Fp>\u003Cp>💬 Finally, each model will be presented with the same \u003Cstrong>prompt\u003C\u002Fstrong>:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable style=\"border: medium; border-collapse: collapse;\">\n  \u003Ccolgroup>\n    \u003Ccol width=\"714\">\n  \u003C\u002Fcolgroup>\n  \u003Ctbody>\n    \u003Ctr>\n      \u003Ctd style=\"border-width: 0.681818pt; border-style: solid; border-color: rgb(0, 0, 0); vertical-align: top; padding: 0pt 5pt; overflow: hidden; overflow-wrap: break-word;\">\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Act as a professional English-to-{TARGET_LANGUAGE} translator specializing in marketing copy for consumer technology and wellness products. I will provide you with a source text, a marketing description for \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Lumi Ring\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, an AI-powered sleep tracking ring.\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">As reference materials, use these files uploaded as {Sources\u002FProject Files\u002Fattached below – depending on the LLM’s UI}:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cul style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A glossary\u003C\u002Fspan>\u003Cspan> (English–{TARGET_LANGUAGE}) with approved terminology\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A style guide\u003C\u002Fspan>\u003Cspan> with brand voice, register, and grammatical rules for {TARGET_LANGUAGE}\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">A translation memory\u003C\u002Fspan>\u003Cspan> with previously approved English–{TARGET_LANGUAGE} segments and their match rates\u003C\u002Fspan>\n          \u003C\u002Fli>\n        \u003C\u002Ful>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Your task:\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> Translate the full source text into {TARGET_LANGUAGE} according to the rules below.\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Reference hierarchy (in order of priority):\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Col style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Translation memory\u003C\u002Fspan>\u003Cspan> — for any segment matching at 100%, reuse the target verbatim. For high fuzzy matches (85–95%), follow the existing translation pattern closely and adapt only what is necessary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Glossary\u003C\u002Fspan>\u003Cspan> — every term listed in the glossary must be translated using the approved form on first use. Acronyms are retained in English with the target-language expansion in parentheses on first use, per the glossary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">\n            \u003Cspan style=\"font-weight: 700;\">Style guide\u003C\u002Fspan>\u003Cspan> — apply all brand voice, register, formatting, and grammatical rules. In case of conflict, the style guide overrides defaults but does not override TM or glossary.\u003C\u002Fspan>\n          \u003C\u002Fli>\n        \u003C\u002Fol>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Translation requirements:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Cul style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve the brand voice: friendly, approachable, confident, warm — never clinical or overly formal.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Use \u003Cspan style=\"font-weight: 700;\">gender-neutral language\u003C\u002Fspan> throughout, following the strategies defined in the style guide. Avoid masculine-default forms when addressing the reader.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Keep brand names \u003Cspan style=\"font-weight: 700;\">invariable\u003C\u002Fspan>: Lumi Ring, Lumi Band Pro, SleepIQ™, Lumi Coach. Do not decline them through grammatical cases.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve \u003Cspan style=\"font-weight: 700;\">sentence rhythm\u003C\u002Fspan>: do not merge short punchy sentences into longer ones for the sake of \"more natural\" target-language prose. Short sentences are intentional brand voice devices.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Preserve \u003Cspan style=\"font-weight: 700;\">all formatting exactly\u003C\u002Fspan>: bold on technical terms, the ™ symbol, the SpO₂ subscript, em dashes, paragraph breaks, bullet points.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Translate idioms and brand taglines for \u003Cspan style=\"font-weight: 700;\">meaning, not word-for-word\u003C\u002Fspan>.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Avoid unnecessary anglicisms when a natural target-language equivalent is in the glossary. Do not leave English terms untranslated unless explicitly retained per the glossary.\u003C\u002Fli>\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">Apply all target-language–specific rules from the style guide, including diacritics, address forms, voice (active\u002Fpassive) preferences, and case\u002Fagreement requirements.\u003C\u002Fli>\n        \u003C\u002Ful>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 8pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Output format:\u003C\u002Fspan>\n        \u003C\u002Fp>\n\n        \u003Col style=\"margin-top: 0pt; margin-bottom: 8pt; padding-left: 24px;\">\n          \u003Cli style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-style: italic; line-height: 1.38;\">The full translation only — no commentary, no explanations.\u003C\u002Fli>\n        \u003C\u002Fol>\n\n        \u003Cp dir=\"ltr\" style=\"line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;\">\n          \u003Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 400; font-style: italic; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Do not summarize the source. Do not add disclaimers about translation difficulty. Translate.\u003C\u002Fspan>\n        \u003C\u002Fp>\n      \u003C\u002Ftd>\n    \u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Cblockquote>\u003Cem>\u003Cstrong>Disclaimer\u003C\u002Fstrong>: The translation quality assessments and comparisons presented in this article are based on a limited set of tests performed by language professionals and should not be considered exhaustive or definitive. Due to constraints such as access to full model capabilities, proprietary algorithms, and the broad range of potential test conditions, our benchmarking efforts may not fully capture the capabilities of each model. We encourage readers to consult additional research and papers that offer more extensive benchmarking analyses. Bear in mind also that models used in these tests are updated and change all the time. Our benchmarking criteria might be limited.\u003C\u002Fem>\u003C\u002Fblockquote>\u003Ch2 id=\"%E2%9A%94%EF%B8%8F-the-great-llm-battle-no-2\">⚔️ The great LLM battle no. 2\u003C\u002Fh2>\u003Cp>Below you can see the result of our great translation battle, categorized by models. First come the three Western LLMs, followed by the Eastern group.\u003C\u002Fp>\u003Ch3 id=\"chatgpt-version-gpt-55-standard-thinking\">\u003Ca href=\"https:\u002F\u002Fchatgpt.com\">ChatGPT\u003C\u002Fa>, version \u003Ca href=\"https:\u002F\u002Fhelp.openai.com\u002Fen\u002Farticles\u002F11909943-gpt-55-in-chatgpt\">GPT-5.5\u003C\u002Fa>, standard thinking\u003C\u002Fh3>\u003Cp>For this and the following model, translations were set up as \u003Cstrong>Projects\u003C\u002Fstrong>. In Polish, all glossary terms landed correctly, and the style guide was applied. On the TM side, the model tried to make an effort, but it was quite selective. For 95% matches, it often reached for its own phrasing instead, for example, it translated “while you rest” as \u003Cem>\"gdy odpoczywasz\"\u003C\u002Fem> rather than \u003Cem>\"podczas odpoczynku\"\u003C\u002Fem> available in the TM.\u003C\u002Fp>\u003Cp>It made similar blunders for \u003Cem>\"dane z czujników\"\u003C\u002Fem> instead of \u003Cem>\"dane czujnikowe\"\u003C\u002Fem> (“sensor data”) or \u003Cem>\"metryki próżności\"\u003C\u002Fem> instead of \u003Cem>\"wskaźniki próżności\"\u003C\u002Fem> (“vanity metrics”) that were expected based on the TM. Most 85% matches were missed entirely. There were three 100% matches available; the model applied two of them and rewrote the third from scratch. And that was the opening sentence – so not something hidden in the middle that could be difficult to recognize.\u003C\u002Fp>\u003Cp>It seems \u003Cstrong>ChatGPT trusted itself more than the abundant TM\u003C\u002Fstrong>. The translation was \u003Cstrong>accurate, but not entirely fluent\u003C\u002Fstrong>, with many awkward or slightly literal phrases.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png\" class=\"kg-image\" alt=\" English to Polish translation on ChatGPT, version GPT-5.5.\" loading=\"lazy\" width=\"934\" height=\"950\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FChat-GPT-Pl-translation.png 934w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, this model provided the most balanced output overall. It ranked high in every single category, and\u003Cstrong> the output required only minor post-editing\u003C\u002Fstrong>, mainly for a few slightly literal formulations.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-ChatGPT.png\" class=\"kg-image\" alt=\" English to Croatian translation on ChatGPT, version GPT-5.5.\" loading=\"lazy\" width=\"1187\" height=\"742\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FHR-ChatGPT.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FHR-ChatGPT.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-ChatGPT.png 1187w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"claude-opus-47\">\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-opus-4-7\">Claude Opus 4.7\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fclaude.ai\u002F\">Claude\u003C\u002Fa> turned out to be \u003Cstrong>the most fluent of the six for the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ftranslate\u002Fen-pl_pl\">Polish translation\u003C\u002Fa>\u003C\u002Fstrong>. The model applied all TM matches and missed only one 75% entry, rendering \u003Cem>\"Wskaźnik stresu autonomicznego\"\u003C\u002Fem> instead of the TM's \u003Cem>\"Autonomiczny wskaźnik stresu\"\u003C\u002Fem>. Short source sentences came out less clumsy than in any other model, with fewer word-for-word calques. One headline tripped it up: \u003Cem>\"Zbudowany na trzech latach Ciebie”\u003C\u002Fem> for “Built on three years of you” was too literal.\u003C\u002Fp>\u003Cp> Terminology and style guide were both applied correctly, hence the overall high rating.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FClaude-PL-translation.png\" class=\"kg-image\" alt=\"English to Polish translation in Claude Opus 4.7.\" loading=\"lazy\" width=\"808\" height=\"932\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FClaude-PL-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FClaude-PL-translation.png 808w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, the model provided strong results but struggled with style-guide compliance. The key issue was the \u003Cstrong>Croatian diacritics and incorrect SpO2 formatting\u003C\u002Fstrong> instead of SpO₂. Overall, the output for this model can be deemed usable for both languages; however, only after careful review.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-translation-in-Claude.png\" class=\"kg-image\" alt=\"English to Croatian translation in Claude Opus 4.7.\" loading=\"lazy\" width=\"1833\" height=\"821\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FHR-translation-in-Claude.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FHR-translation-in-Claude.png 1833w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"gemini-3-thinking\">\u003Ca href=\"https:\u002F\u002Fblog.google\u002Fproducts-and-platforms\u002Fproducts\u002Fgemini\u002Fgemini-3\u002F#gemini-3-deep-think\">Gemini 3 Thinking\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Since no project feature was available here, the files and instructions were uploaded together with the prompt. In Polish, \u003Ca href=\"https:\u002F\u002Fgemini.google.com\u002F\">the model\u003C\u002Fa> showed a couple of accuracy issues. \"Have been interpreting\" came out as \u003Cem>\"interpretowała\"\u003C\u002Fem> – a verb rendered in past tense, where \u003Cem>\"interpretuje\"\u003C\u002Fem> (present tense) would have been more appropriate in this context. A grammar slip appeared in one string, and the TM also took some hits. \u003Cstrong>The model failed to replicate a few phrases from the memory\u003C\u002Fstrong>, missing similar entries to ChatGPT. Terminology and style guide were otherwise applied correctly.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FPL-Gemini.png\" class=\"kg-image\" alt=\"English to Polish translation in Gemini 3 Thinking.\" loading=\"lazy\" width=\"911\" height=\"793\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FPL-Gemini.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FPL-Gemini.png 911w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Croatian, \u003Cstrong>the model ranked high for fluency but ignored the style guide on several occasions\u003C\u002Fstrong>. For example, the phrase \u003Cem>“fotopletizmografija (PPG) senzor”\u003C\u002Fem> wasn’t rendered correctly. A more idiomatic option would be \u003Cem>“senzor za fotopletizmografiju (PPG)”\u003C\u002Fem> or \u003Cem>“senzor fotopletizmografije (PPG)”\u003C\u002Fem>.  The term “SpO₂” wasn’t formatted as per the style guide, some phrases included agreement problems (e.g., in \u003Cem>“Dostupno sada u bojama Midnight, Chalk i Sand.”\u003C\u002Fem>), and a few technical terms came out with awkward phrasing.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FGemini-HR-translation.png\" class=\"kg-image\" alt=\"English to Croatian translation in Gemini 3 Thinking.\" loading=\"lazy\" width=\"1023\" height=\"763\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FGemini-HR-translation.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FGemini-HR-translation.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FGemini-HR-translation.png 1023w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"deepseek-r1-deepthinkexpert\">\u003Ca href=\"https:\u002F\u002Fapi-docs.deepseek.com\u002Fnews\u002Fnews250120\">DeepSeek R1\u003C\u002Fa> (DeepThink\u002FExpert)\u003C\u002Fh3>\u003Cp>There is no project feature here either, so files and instructions were uploaded together with the prompt. For both translation tasks, \u003Cstrong>the model took the longest time to think\u003C\u002Fstrong>.\u003Cstrong> \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ftranslate\u002Fen-hr_hr\">Croatian translation\u003C\u002Fa> appeared after approximately 6 minutes, while for other models, the thinking time never exceeded 3 minutes. Before displaying the Polish translation, \u003Ca href=\"https:\u002F\u002Fwww.deepseek.com\u002Fen\u002F\">the model\u003C\u002Fa> thought for 211 seconds. This time was not spent recklessly, though. \u003Cstrong>Deepseek turns out to be the most thorough on TM entries in both languages\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>For example, in Polish, all matches were applied correctly, including the low ones and the tricky entry with \u003Cem>\"autonomiczny wskaźnik stresu\"\u003C\u002Fem> (“autonomic stress index“) that tripped up most other models. \u003C\u002Fp>\u003Cp>In Croatian, the model applied the TM, style guide, and glossary with surgical precision, delivering the second-best translation.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png\" class=\"kg-image\" alt loading=\"lazy\" width=\"857\" height=\"937\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-PL-thinking.png 857w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>It was not all roses, though, and both languages included minor blunders. In Croatian, some phrases were \u003Cstrong>slightly less natural from the marketing perspective\u003C\u002Fstrong>. One good example of this issue can be found in \u003Cem>“na temelju vaše akumulacije duga spavanja u stvarnom vremenu”\u003C\u002Fem>. The phrase was translated accurately, but sounds nothing like the approachable technical language prescribed in the style guide. A lighter Croatian marketing version would be \u003Cem>“na temelju vašeg stvarnog nakupljanja duga spavanja”\u003C\u002Fem>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png\" class=\"kg-image\" alt=\"English to Croatian translation in DeepSeek R1.\" loading=\"lazy\" width=\"1030\" height=\"890\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FDeepSeek-thought-for-320-s-HR.png 1030w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>In Polish, there were minor grammar mistakes, literal phrasing, and a few clumsy constructions. Gender neutrality was missed for \u003Cem>\"pracowników\" \u003C\u002Fem>(“workers”), but other requirements of the style guide and terminology were applied correctly.\u003C\u002Fp>\u003Cp>\u003Cstrong>It seems that what DeepSeek gained in precision, it lost in fluency\u003C\u002Fstrong>.\u003C\u002Fp>\u003Ch3 id=\"qwen-36-plus\">\u003Ca href=\"https:\u002F\u002Fqwen.ai\u002Fblog?id=qwen3.6\">Qwen 3.6-Plus\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fchat.qwen.ai\">This model\u003C\u002Fa> offers a Project feature, so reference files were uploaded as project files. It also took longer to analyze the file, compared to the Western group models. In Polish, the TM compliance was solid with only one low-match entry missed \u003Cem>(\"Autonomiczny wskaźnik stresu\")\u003C\u002Fem>. The problem was fluency. Clumsy compounds like \u003Cem>\"mikro-wybudzenia\"\u003C\u002Fem>, awkward phrase order in \u003Cem>\"faza lekkiego snu\"\u003C\u002Fem> for \u003Cem>“light sleep transitions\u003Cstrong>”\u003C\u002Fstrong>\u003C\u002Fem>, and constructions like \u003Cem>\"kontekst do działania\" (“Actionable context”)\u003C\u002Fem> and \u003Cem>\"stres późnonocny\"\u003C\u002Fem> \u003Cem>(“late-night stress “)\u003C\u002Fem> made the text feel unpolished. Passive voice appeared where it wasn't needed. \u003Cstrong>The model translated accurately, but not as a professional native translator would\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-PL.png\" class=\"kg-image\" alt=\"English to Polish translation in Qwen 3.6-Plus.\" loading=\"lazy\" width=\"1153\" height=\"888\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FQwen-PL.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FQwen-PL.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-PL.png 1153w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>Things didn’t look up for the Croatian translation either. Overall, \u003Cstrong>Qwen provided the weakest output for this language pair\u003C\u002Fstrong>. Grammar hiccups, case-related conflict, and missing context were the key issues behind the lowest ranking. One example of issues in this category is the phrase \u003Cem>“na temelju vašeg dug spavanja u stvarnom vremenu”\u003C\u002Fem>, which is not grammatically correct. After \u003Cem>“vašeg”\u003C\u002Fem>, the noun phrase must agree in case, so the correct form would be \u003Cem>“vašeg duga spavanja”\u003C\u002Fem> or, more naturally in this context, \u003Cem>“vašeg stvarnog nakupljanja duga spavanja”\u003C\u002Fem>.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-HR-trans.png\" class=\"kg-image\" alt=\"English to Croatian translation in Qwen 3.6-Plus.\" loading=\"lazy\" width=\"1646\" height=\"827\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FQwen-HR-trans.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2026\u002F05\u002FQwen-HR-trans.png 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2026\u002F05\u002FQwen-HR-trans.png 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FQwen-HR-trans.png 1646w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch3 id=\"kimi-k26-thinking\">\u003Ca href=\"https:\u002F\u002Fwww.kimi.com\u002Fai-models\u002Fkimi-k2-6\">Kimi K2.6 Thinking\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Like DeepSeek, \u003Ca href=\"https:\u002F\u002Fwww.kimi.com\">this model\u003C\u002Fa> spent a lot of time on thinking (nearly 9 minutes), and all instructions plus files had to be fed as a simple prompt because of the \u003Cstrong>missing Projects feature\u003C\u002Fstrong>. In the case of the Polish translation, the thinking time was rewarded with \u003Cstrong>probably the most creative translation in the set\u003C\u002Fstrong>. It was less literal than ChatGPT or Gemini, with noticeably better flow. On TM, Kimi missed only one low-match entry \u003Cem>(\"wskaźniki stresu autonomicznego\")\u003C\u002Fem>. Two issues pulled the score down. The first one was the gendered forms for \u003Cem>\"podróżników\"\u003C\u002Fem> and \u003Cem>\"pracowników\"\u003C\u002Fem>, although the style guide required neutral phrasing. Another one was a fluency glitch in the final two sentences, both of which contained the same word \u003Cem>(\"już\")\u003C\u002Fem>, a repetition that a quick read-through would have caught. Or one that a professional translator wouldn’t have made at all.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-PL.png\" class=\"kg-image\" alt=\"English to Polish translation in Kimi K2.6 Thinking.\" loading=\"lazy\" width=\"908\" height=\"935\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FKimi-PL.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-PL.png 908w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Cp>For the Croatian translation, the model generated natural and marketing-friendly text, doing its best to apply all the provided resources. The TM and glossary compliance were strong, but \u003Cstrong>it struggled with fluency\u003C\u002Fstrong>. There were linguistic and structural issues, including case\u002Fagreement problems and changes to line breaks.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-HR.png\" class=\"kg-image\" alt=\"English to Croatian translation in Kimi K2.6 Thinking.\" loading=\"lazy\" width=\"827\" height=\"895\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2026\u002F05\u002FKimi-HR.png 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2026\u002F05\u002FKimi-HR.png 827w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch2 id=\"%F0%9F%93%8B-what%E2%80%99s-the-llm-leaderboard\">📋 What’s the LLM leaderboard?\u003C\u002Fh2>\u003Cp>This is how each LLM scored for each indicator and language combination:\u003C\u002Fp>\u003C!--kg-card-begin: markdown-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth style=\"text-align:center\">\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with  translation  memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with the  style guide\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Contextual  appropriateness\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Claude\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">28\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Kimi\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">DeepSeek\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Qwen\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">27\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Gemini\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">ChatGPT\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C!--kg-card-end: markdown-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 1\u003C\u002Fstrong>. LLM's scores for the Polish translation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C!--kg-card-begin: markdown-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth style=\"text-align:center\">\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with  translation  memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Coherence across paragraphs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Consistency  with the  style guide\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Contextual  appropriateness\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth style=\"text-align:center\">\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">ChatGPT\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">5\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">DeepSeek\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Claude\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">23\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Qwen\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">21\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Gemini\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Kimi\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">4\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C!--kg-card-end: markdown-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 2\u003C\u002Fstrong>. LLM's scores for the Croatian translation.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cp>\u003Cstrong>History repeats itself. battle no. 1 ended with no clear winner, and so does battle no. 2\u003C\u002Fstrong>. All tested LLMs turned out to be extremely powerful and capable of rendering correct translations in Polish and Croatian, based on the provided context. No matter the setup (whether it was a project or a simple prompt with all files attached), the models accessed style guides, glossaries, and translation memories, spent some time analyzing the context, followed the prompt accurately, and created content that was not only linguistically correct, but also, in most cases, in line with the brand rules.\u003C\u002Fp>\u003Cp>\u003Cstrong>ChatGPT was the least accurate\u003C\u002Fstrong> when it comes to reusing entries from the Translation Memory, while \u003Cstrong>DeepSeek was the most precise\u003C\u002Fstrong> in this aspect, applying all matches, even those below 80%. Glossary and style guide enforcement (excluding the rule on gender neutrality) was handled correctly by most models in both languages.\u003C\u002Fp>\u003Cblockquote>This time, all tested LLMs were extremely powerful and capable of rendering correct translations in both Polish and Croatian when given the context. Guides, glossaries and TMs were used, and the content created based on them was mostly in line with brand rules\u003C\u002Fblockquote>\u003Cp>Of course, this doesn't mean that the translations were perfect. While there were no big issues in terms of accuracy, \u003Cstrong>fluency varied, leaving room for improvement\u003C\u002Fstrong>. Literal phrases and grammatically awkward constructions appeared in nearly every output to a different extent. Although the texts could have been infused with more creativity and personality, they were mostly on brand and aligned with the provided context.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%86%9A-east-or-west-what%E2%80%99s-the-best\">🆚 East or West: what’s the best?\u003C\u002Fh2>\u003Cp>When it comes to the Western vs. Eastern divide, our test shows that \u003Cstrong>DeepSeek, Qwen, and Kimi generate results comparable with the frontier models\u003C\u002Fstrong>, at least for the languages and content selected for our experiment.\u003C\u002Fp>\u003Cp>There is no clear winner in this group. The interesting part, however, is that \u003Cstrong>for Polish translation, the Eastern models slightly outperformed ChatGPT and Gemini\u003C\u002Fstrong>, \u003Cstrong>while Claude led\u003C\u002Fstrong> the overall leaderboard. \u003Cstrong>For Croatian, it was ChatGPT that took the spot no. 1\u003C\u002Fstrong>, followed by DeepSeek and Claude.\u003C\u002Fp>\u003Cp>Simply put, the East retains its strong position thanks to DeepSeek, and the West shines through strong results from \u003Cstrong>ChatGPT (for Croatian)\u003C\u002Fstrong> and from \u003Cstrong>Claude (for Polish)\u003C\u002Fstrong>. These results may, of course, vary depending on the language pairs, content type, and the quality of the provided context.\u003C\u002Fp>\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-yellow\">\u003Cdiv class=\"kg-callout-emoji\">👉\u003C\u002Fdiv>\u003Cdiv class=\"kg-callout-text\">Compared to our first LLM Translation War released in 2025, in the case of the Polish translation, DeepSeek and Qwen have retained their strong position, while Claude gained in strength, coming from place no. 5 to no. 1. Claude won the ranking for Croatian in 2025; now this model dropped to spot no. 3, while ChatGPT moved from no. 4 to no. 1.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Ch2 id=\"%F0%9F%8F%86-and-the-winner-is%E2%80%A6\">🏆 And the winner is…\u003C\u002Fh2>\u003Cp>What does it all mean for your content? Which LLM should you choose for your translations?\u003C\u002Fp>\u003Cp>There’s no universal solution, but one thing is sure: the future is integrated. No one will spend time and tokens to manually run multiple prompts for multiple language pairs in several LLMs, and with several reference documents attached to the prompt. \u003Cstrong>For the best result, you need a system that lets you easily choose the model that delivers the best result for your language and content type\u003C\u002Fstrong>, upload context that stays in the system and is reusable, when necessary, plus provide instruction that can be applied to other projects. That’s exactly what an orchestrated, agentic system offers.\u003C\u002Fp>\u003Cp>One such solution is Localazy: you upload your glossary, add TM and a style guide, and \u003Cstrong>run pre-translation using their proprietary model, \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Flocalazy-ai\">Localazy AI\u003C\u002Fa>\u003C\u002Fstrong>, which has been fine-tuned to provide the best for localization use cases. The same context materials can be used for translation into several languages, so there’s no need to create a separate project per language or copy-paste your prompt in the LLM’s interface several times. \u003Cstrong>Everything is connected, easy to use, and transparent when it comes to token use\u003C\u002Fstrong>. \u003C\u002Fp>\u003Cp>If that sounds too good to be true, have a look at the test I’ve performed recently to find out if \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Flocalazy-ai-put-to-the-test-across-three-content-types\">Localazy AI can handle the context correctly\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%92%AC-faqs\">💬 FAQs\u003C\u002Fh2>\u003Ch3 id=\"have-chinese-llms-improved-since-2025\">Have Chinese LLMs improved since 2025?\u003C\u002Fh3>\u003Cp>According to\u003Ca href=\"https:\u002F\u002Fwww.scmp.com\u002Ftech\u002Ftech-trends\u002Farticle\u002F3335602\u002Fchinas-open-source-models-make-30-global-ai-usage-led-qwen-and-deepseek\"> research from OpenRouter and Andreessen Horowitz\u003C\u002Fa>, Chinese open-source models went from roughly 1.2% of global AI usage in late 2024 to nearly 30% by the end of 2025. Their performance has improved significantly. For example, \u003Ca href=\"https:\u002F\u002Fwww.e2enetworks.com\u002Fblog\u002Fdeepseek-v3-2-open-source-reasoning\">DeepSeek’s V3.2-Speciale model won gold medals\u003C\u002Fa> in the International Mathematical Olympiad and the International Olympiad in Informatics. \u003C\u002Fp>\u003Cp>According to\u003Ca href=\"https:\u002F\u002Fbenchlm.ai\u002Fblog\u002Fposts\u002Fbest-chinese-llm\"> BenchLM.ai\u003C\u002Fa>, the gap between the Chinese model and the global frontier is shrinking. Number one Chinese LLM is DeepSeek V4 Pro (Max), followed by DeepSeek V4 Pro (High) and Kimi K2.6. \u003Ca href=\"https:\u002F\u002Fwww.nist.gov\u002Fnews-events\u002Fnews\u002F2026\u002F05\u002Fcaisi-evaluation-deepseek-v4-pro\">CAISI Evaluation\u003C\u002Fa> assessed that DeepSeek V4’s capabilities lag behind the frontier models by about 8 months. This assessment also confirms that DeepSeek V4 is more cost-efficient than other models of similar capability.\u003C\u002Fp>\u003Ch3 id=\"are-chinese-ai-models-as-good-as-western-models-for-translation\">Are Chinese AI models as good as Western models for translation?\u003C\u002Fh3>\u003Cp>Our tests prove that the top Chinese models (especially DeepSeek) are comparable to the Western models when it comes to translation. With the relevant context and precise instructions, these models are able to generate decent results.\u003C\u002Fp>\u003Ch3 id=\"which-llm-is-best-for-localizing-a-mobile-app\">Which LLM is best for localizing a mobile app?\u003C\u002Fh3>\u003Cp>There’s no one-size-fits-all solution. The results will vary depending on the language. According to the findings of the \u003Ca href=\"https:\u002F\u002Faclanthology.org\u002Fanthology-files\u002Fpdf\u002Fwmt.real\u002F2025.wmt-1.1.pdf\">WMT25 General Machine Translation Shared Task\u003C\u002Fa>, the best system overall was Gemini 2.5 Pro (it ended up in the top cluster in 14 language pairs). Our experiments from the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fthe-great-llm-translation-war-a-comparison-of-the-hottest-ai-models\">Translation War no. 1 \u003C\u002Fa> focused on mobile apps showed that DeepSeek is the top model for localizing mobile apps into Polish, and Claude for Croatian.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fintento-releases-9th-annual-state-of-translation-automation-2025-industry-report-302598803.html\">The Intento report\u003C\u002Fa> highlights that it’s the multi-agent workflow that delivers the best results. Bear in mind that top models constantly get new updates, and their results change. What’s the best varies depending on whether you prioritize translation accuracy, brand voice, terminology consistency, creativity, or privacy constraints. Whatever you choose, don’t forget about the human review,\u003Ca href=\"https:\u002F\u002Finternationalachieversgroup.com\u002Flocalisation\u002Fmetrics-for-evaluating-machine-translation-using-llms\u002F\"> as studies show tone, nuance, and cultural fit still need careful oversight\u003C\u002Fa>.\u003C\u002Fp>","public","none","2026-05-25T16:35:43.000+02:00",[1946],{"id":1817,"name":1818,"slug":1819,"profile_image":1820,"cover_image":8,"bio":1947,"website":1948,"location":1949,"facebook":8,"twitter":8,"meta_title":8,"meta_description":8,"url":1950},"Localization consultant, prompt engineer, and translator specializing in IT. I teach online courses on localization and enjoy writing about AI.","http:\u002F\u002Fwww.dorotapawlak.eu","Amsterdam, Netherlands","https:\u002F\u002Fghost.localazy.com\u002Fauthor\u002Fdorota-pawlak\u002F",[1952,1958,1961,1966,1971,1974,1979,1982,1987,1992],{"id":1953,"name":1954,"slug":1955,"description":1956,"feature_image":8,"visibility":1942,"og_image":8,"og_title":8,"og_description":8,"twitter_image":8,"twitter_title":8,"twitter_description":8,"meta_title":8,"meta_description":8,"codeinjection_head":8,"codeinjection_foot":8,"canonical_url":8,"accent_color":8,"url":1957},"6983306c63350c0001d634cf","[AD] Localazy 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