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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",[1882,1885,1888,1891,1894],{"id":1883,"slug":1884},169,"ai-powered-translation",{"id":1886,"slug":1887},293,"natural-language-processing-nlp",{"id":1889,"slug":1890},304,"language-modeling",{"id":1892,"slug":1893},439,"tiny-language-model-tlm",{"id":1895,"slug":1896},440,"small-language-model-slm",{"id":1898,"status":5,"owner":1875,"created_on":1876,"title":1899,"excerpt":1900,"content":1901,"slug":1902,"meta_title":1788,"meta_description":1788,"canonical":1788,"related_terms":1903},189,"Neural Machine Translation (NMT)","A type of automated translation that uses artificial neural networks to predict the likelihood of a sequence of words, producing translations fast.\n","Neural Machine Translation (NMT) is an advanced approach to automated translation that uses artificial neural networks to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.\n\nNMT is a key component in the field of [machine translation](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fmachine-translation) and represents a significant advancement in [AI-powered translation](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fai-powered-translation) technology.\n\nUnlike earlier statistical methods, NMT can capture context and nuances in language, often resulting in more fluent and contextually appropriate translations.\n\n## 🧠 Key points about NMT:\n\n* NMT utilizes deep learning techniques, particularly Recurrent Neural Networks (RNNs) and transformers, to model the translation process. These systems learn to translate directly from source to target language without relying on extensive feature engineering.\n* While initially challenged by rare words, techniques like subword tokenization have improved NMT's ability to handle diverse vocabularies. NMT models can consider the entire input sentence, allowing for better handling of context and long-range dependencies.\n* Training NMT models typically requires significant computational resources and large parallel corpora.\n* Many NMT models incorporate attention mechanisms, allowing the system to focus on relevant parts of the input sentence when generating each word of the translation.\n* Pre-trained NMT models can be adapted to new language pairs or domains, reducing the need for extensive language-specific data.\n* Some models are trained on multiple language pairs simultaneously, enabling zero-shot translation between language pairs not seen during training.\n\nNMT has significantly improved the quality of machine translation across many language pairs and continues to be a primary focus of research and development in the field of AI-powered language technologies.","neural-machine-translation",[1904,1905,1907,1908,1911,1914],{"id":71,"slug":1402},{"id":251,"slug":1906},"machine-learning-ml",{"id":1883,"slug":1884},{"id":1909,"slug":1910},186,"statistical-machine-translation",{"id":1912,"slug":1913},187,"example-based-machine-translation",{"id":1915,"slug":1916},188,"hybrid-machine-translation",{"id":71,"status":5,"owner":1918,"created_on":1919,"title":1920,"excerpt":1921,"content":1922,"slug":1402,"meta_title":1788,"meta_description":1788,"canonical":1788,"related_terms":1923},"2bac48a8-b362-482f-b574-3bc71dca4c5b","2026-05-12T07:09:22.000Z","Machine translation (MT)","The process of translating using computational linguistics.","A machine translation engine is a software tool that translates a text from one language to another without the help of human translators.\n\nMachine translation, also known as MT, is widely used as the first round of translations and is frequently very effective for short strings. However, it can produce unsatisfactory results when context knowledge is required, and thus, a human review is always advised.\n\n### 💬 Methods used in machine translation\n\nMachine translation engines recently experienced huge improvements related to the advanced use of neural networks, but they generally rely on these different methods:\n\n* [Statistical machine translation (SMT)](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fstatistical-machine-translation\u002F \"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fstatistical-machine-translation\u002F\")\n* [Example-based machine translation (EBMT)](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fexample-based-machine-translation \"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fexample-based-machine-translation\")\n* [Hybrid machine translation (HMT)](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fhybrid-machine-translation \"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fhybrid-machine-translation\")\n* [Neural machine translation (NMT)](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fneural-machine-translation\u002F \"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fneural-machine-translation\u002F\")\n\nNeural machine translation (used by [AI-powered translation](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fai-powered-translation) engines) is the most sophisticated type of instant translation today. It uses machine learning to improve its translation capabilities. As the engine translates more text with each request, the better results it produces with time by learning. Most instant translation services use this technology.\n\n### 🔍 What's the difference between HAMT and MAHT?\n\nWithin the realm of machine translation, it’s important to distinguish between **Human-Assisted Machine Translation (HAMT)** and **Machine-Assisted Human Translation (MAHT)**:\n\n* [**Human-Assisted Machine Translation (HAMT)**](\u002Fdictionary\u002Fhuman-assisted-mt): This process involves a human reviewing translations generated by machine translation (MT) engines. It is also known as MT plus post-editing and\u002For pre-editing. The machine handles the initial translation, which is then edited by a human to improve accuracy and context. This approach is useful for projects with repetitive content and where budget or time constraints are significant.\n* [**Machine-Assisted Human Translation (MAHT)**](\u002Fdictionary\u002Fmaht): In this method, human translators use computer software to aid their work. Translators receive machine translation suggestions, but they decide how to use them. This approach allows for high translation accuracy while benefiting from features like translation glossaries and memories, which enhance the efficiency and consistency of the translation process.\n\n### 🦾 MT engines available in Localazy \n\nLocalazy users can use the most popular and advanced [MT engines](https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fmachine-translation \"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fmachine-translation\") to translate their content:\n\n* Amazon Translate (available for free)\n* Google Translate\n* DeepL\n* Azure Translator\n* OpenAI (ChatGPT), with your own API key\n\n> *Users can unlock different engines depending on the plan they buy. See our [pricing](https:\u002F\u002Flocalazy.com\u002Fpricing) and [docs](https:\u002F\u002Flocalazy.com\u002Fdocs\u002Fgeneral\u002Fadditional-mt-engines?srsltid=AfmBOoo--lURSBHDiHEoe3rYL3xSQHaZcTsl6kd7wwUYRGC-sGTTmG94 \"https:\u002F\u002Flocalazy.com\u002Fdocs\u002Fgeneral\u002Fadditional-mt-engines?srsltid=AfmBOoo--lURSBHDiHEoe3rYL3xSQHaZcTsl6kd7wwUYRGC-sGTTmG94\") for more details.*\n\n### 📚 Additional resources:\n\n* [What is machine translation?](https:\u002F\u002Fwww.gala-global.org\u002Fwhat-machine-translation)\n* [History of machine translation](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHistory_of_machine_translation)",[1924,1926,1928,1930],{"id":513,"slug":1925},"maht",{"id":988,"slug":1927},"mtpe-machine-translation-post-editing",{"id":917,"slug":1929},"deepl",{"id":1931,"slug":1932},136,"human-assisted-mt",{"id":1883,"status":5,"owner":1934,"created_on":1935,"title":1936,"excerpt":1937,"content":1938,"slug":1884,"meta_title":1788,"meta_description":1788,"canonical":1788,"related_terms":1939},"d8c4ae49-7239-4b85-abb6-d1fa80ba09ad","2024-07-11T05:52:24.000Z","AI-powered translation","A type of automated translation that leverages artificial intelligence and machine learning technologies to automatically translate content from one language to another.","This approach enhances the speed and accuracy of translations by continuously learning and improving from large datasets.\n\n**AI-powered translation** is a subset of [Machine Translation](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fmachine-translation) closely related to [neural networks](https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fneural-machine-translation\u002F \"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Fneural-machine-translation\u002F\") that has evolved significantly with the advancements in AI. 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).*",[1940,1941],{"id":71,"slug":1402},{"id":1898,"slug":1902},{"id":45,"status":5,"owner":1918,"created_on":1919,"title":1943,"excerpt":1944,"content":1945,"slug":1417,"meta_title":1788,"meta_description":1788,"canonical":1788,"related_terms":1946},"Translation Memory (TM)","A database of previously translated terms that reduces translation costs and enhances productivity by reusing translations.","[**Translation Memory (TM)**](https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Ftranslation-memory\u002F \"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Ftranslation-memory\u002F\") is an essential feature that helps store and reuse translated terms. By avoiding the duplication of identical or similar texts, a translation memory makes the whole translation process of your project faster and cheaper.\n\nAt Localazy, you can use ShareTM to assist you in providing suggestions based on previously approved translations. [**ShareTM**](https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fshare-tm\u002F \"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fshare-tm\u002F\") allows you to share your translations with the Localazy community and, in return, use translations from other projects. [When you opt into ShareTM](https:\u002F\u002Flocalazy.com\u002Fdocs\u002Fgeneral\u002Fwhat-is-localazy-sharetm?srsltid=AfmBOooCoA89mWglcxDKkVCWdJzTKliFNo696PoS5VxJJFAD6QY_r2yv \"https:\u002F\u002Flocalazy.com\u002Fdocs\u002Fgeneral\u002Fwhat-is-localazy-sharetm?srsltid=AfmBOooCoA89mWglcxDKkVCWdJzTKliFNo696PoS5VxJJFAD6QY_r2yv\"), your translations contribute to a shared memory pool, and in return, you receive suggestions from this broader database. This collaborative approach can speed up the translation process by leveraging the work already done by others.\n\n### 💪 Main benefits of a TM:\n\n* **Cost reduction**: By reusing existing translations, you save on translation costs and resources.\n* **Consistency**: Ensures consistent terminology and phrasing across all your translations.\n* **Efficiency**: Speeds up the translation process by providing instant suggestions.\n* **Collaboration**: In the case of ShareTM, it promotes a collaborative environment where translations are shared and improved collectively, benefiting all users.",[1947,1950,1952],{"id":1948,"slug":1949},266,"crowdsourcing",{"id":19,"slug":1951},"intm",{"id":4,"slug":1953},"sharetm",{"id":155,"status":5,"owner":1918,"created_on":1955,"title":1956,"excerpt":1957,"content":1958,"slug":1959,"meta_title":10,"meta_description":10,"canonical":10,"related_terms":1960},"2022-03-17T12:22:55.000Z","TMX - Translation Memory eXchange format","Translation Memory eXchange (TMX) is an XML specification that allows computer-aided translation and localization tools to exchange translation memory data.\n","Translation Memory eXchange (TMX) is a [translation memory](\u002Fdictionary\u002Ftranslation-memory) format based on XML designed to enable the exchange of translation memory data between CAT tools, translation vendors, and localization platforms.\n\nOSCAR (Open Standards for Container\u002FContent Allowing Re-use), a Localisation Industry Standards Association (LISA) special interest group, has developed and maintained TMX until 2007. The standards were later moved under a Creative Commons license.\n\nLearn more about the [TMX format specifications](https:\u002F\u002Ffileinfo.com\u002Fextension\u002Ftmx)","tmx-translation-memory-exchange-format",[1961,1963,1964],{"id":33,"slug":1962},"translation",{"id":45,"slug":1417},{"id":238,"slug":1965},"translation-management-system-tms","Choosing the best AI tool to help you localize is becoming harder every day. With new models popping up everywhere, what should you look at to make a decision? We tested a few to get clarity. ","\u003Cp>To LLM or not to LLM?\u003C\u002Fp>\u003Cp>And more importantly: \u003Cstrong>which LLM?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>If you can’t make your head around the recent changes in the Large Language Models (LLMs) scene, fear not. You’re not alone.\u003C\u002Fp>\u003Cp>With over 60 large language models (LLMs) \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FList_of_large_language_models\">available on the market\u003C\u002Fa>, it’s easy to get lost in the labyrinth of choices. The scene gets more crowded month after month, as new models enter the game. ☔️ No wonder users around the globe keep pondering whether these new additions can beat the well-known giants.\u003C\u002Fp>\u003Cp>If these users happen to work in the translation and localization industry, the first question that comes to mind is: \u003Cstrong>Can I successfully use these new LLMs for translation tasks?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>You’ll find the answer in our comprehensive test below.\u003C\u002Fp>\u003Ch2 id=\"new-kids-on-the-block\">🛝 New kids on the block \u003Ca class=\"markdownit-header-anchor\" href=\"#new-kids-on-the-block\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>Earlier this year, \u003Ca href=\"https:\u002F\u002Fdaily.dev\u002Fblog\u002Fdeepseek-everything-you-need-to-know-about-this-new-llm-in-one-place\">\u003Cstrong>DeepSeek\u003C\u002Fstrong> made it to the headlines with a splash\u003C\u002Fa>. However, that’s not the only LLM that has recently launched. There are dozens of other newcomers, too.\u003C\u002Fp>\u003Cp>While \u003Ca href=\"https:\u002F\u002Fchat.deepseek.com\u002Fsign_in\">DeepSeek\u003C\u002Fa> \u003Cstrong>promises high performance, open-source accessibility, high efficiency, and low computational costs, it raises significant data privacy concerns\u003C\u002Fstrong>. For starters, it stores user information on servers located in China and fails to guarantee that EU users' rights under GDPR will be met. But that’s not all. The company collects various types of user data, including text inputs, prompts, uploaded files, chat history, device information, and even keystroke patterns. If that doesn’t sound like the scenes from the \u003Cem>Black Mirror\u003C\u002Fem> series coming to life, I don’t know what else does.\u003C\u002Fp>\u003Cp>As if these weren’t enough, \u003Cstrong>DeepSeek tends to be highly biased\u003C\u002Fstrong>. Prompt it to give you information on any topic censored by the Chinese government, and you’ll find yourself in a loop of diversions. 👀  Cultural Revolution in China? It’s beyond the tool’s scope. Who owns the Spratly Islands? Let’s switch topics.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-censorship.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1042\" height=\"954\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 1042w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Censorship in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cp>Questions about Chinese matters are not the only issues that DeepSeek likes to cover with silence. \u003Ca href=\"https:\u002F\u002Fwww.euronews.com\u002Fnext\u002F2025\u002F01\u002F31\u002Fharmful-and-toxic-output-deepseek-has-major-security-and-safety-gaps-study-warns\">A study by Enkrypt AI\u003C\u002Fa> found that 83% of bias tests on DeepSeek-R1 resulted in discriminatory output, with biases detected across race, gender, health, and religion. Whatever you plan to do with the tool, \u003Cstrong>make sure to double-check the facts\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.doubao.com\u002Fchat\u002F\">\u003Cstrong>Doubao\u003C\u002Fstrong>\u003C\u002Fa>, another Chinese LLM released in January 2025 by ByteDance (the creators of TikTok), shows a similar behavior when it comes to biases and censorship. However, \u003Cstrong>it promises performance that outshines GPT-4o or Claude\u003C\u002Fstrong> in areas such as knowledge retention, coding, reasoning, and Chinese language processing. It also \u003Ca href=\"https:\u002F\u002Fmedium.com\u002Ftowards-agi\u002Fbytedance-just-dropped-doubao-1-5-pro-the-ai-model-thats-quietly-outsmarting-everyone-b9ebc523558d\">scores higher in terms of operational efficiency\u003C\u002Fa>, using merely USD 0.11 per million tokens for input and USD 0.013 for output. \u003Cstrong>The industry giants have on average 70% higher costs\u003C\u002Fstrong>. 📈\u003C\u002Fp>\u003Cp>Cost-effectiveness is the secret weapon of yet another Chinese LLM. \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fchat.qwen.ai\">Qwen\u003C\u002Fa>\u003C\u002Fstrong> was published by Alibaba in 2023 and received its most powerful version, Qwen2.5-Max, in January this year. This model supports multilingual tasks across more than 29 languages and promises \u003Cstrong>excellent long-form text generation, outstanding coding proficiency, and mathematical reasoning\u003C\u002Fstrong>. It’s also fast and efficient. In terms of speed, \u003Ca href=\"https:\u002F\u002Fwww.amitysolutions.com\u002Fblog\u002Fqwen-2-5-ai-breakthrough-all-records\">Qwen matches GPT-4o and outperforms Gemini 1.5\u003C\u002Fa>.\u003C\u002Fp>\u003Cblockquote>Doubao shines in operational efficiency, with considerably lower costs than the LLM giants. Qwen scores high in data privacy, in contrast to DeepSeek, which raises security concerns and tends to be highly biased\u003C\u002Fblockquote>\u003Cp>Unlike DeepSeek, \u003Cstrong>Qwen scores high in data privacy \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Fwww.tomsguide.com\u002Fai\u002Fi-tested-deepseek-vs-qwen-2-5-with-7-prompts-heres-the-winner\">\u003Cstrong>and seems to be less biased\u003C\u002Fstrong>\u003C\u002Fa>. It correctly answers questions about Chinese or international history and shows fewer biases in other areas. 🧑‍🏫 Another good news is that Qwen aligns with GDPR and other global privacy standards.\u003C\u002Fp>\u003Cp>If it all sounds too good to be true, here’s a reason to temper your expectations: Qwen2.5-Max also has its downsides. It’s not an open-source LLM and doesn’t provide transparent reasoning behind its responses, as \u003Ca href=\"https:\u002F\u002Fwww.webelight.com\u002Fblog\u002Fdeepseek-cyber-attack-a-wake-up-call-for-the-AI-industry\" rel=\"noopener noreferrer nofollow\">DeepSeek R1\u003C\u002Fa> or OpenAI’s o1 do. That’s not necessarily a deal-breaker, but something to consider depending on your needs for transparency and adaptability.\u003C\u002Fp>\u003Cp>With so many options, \u003Cstrong>choosing the right LLM for translation sounds like an impossible mission\u003C\u002Fstrong>. It’s tough to decide which model will help you break language barriers and facilitate all those complex localization and internationalization processes.\u003C\u002Fp>\u003Cblockquote>🎙️ To what extent are AI tools useful in localization and translation? \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fs02-ep-01-bridging-the-gap-podcast\">Listen to our conversation with Dialect’s CEO Jiří Šebek\u003C\u002Fa>  for an insider take on this topic\u003C\u002Fblockquote>\u003Ch3 id=\"llm-giants-or-the-shiny-new-chinese-toys\">LLM giants or the shiny new Chinese toys? \u003Ca class=\"markdownit-header-anchor\" href=\"#llm-giants-or-the-shiny-new-chinese-toys\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Let’s put the models to the test. \u003Cstrong>We’ll analyze the translation quality\u003C\u002Fstrong> \u003Cstrong>delivered by the new models\u003C\u002Fstrong> and compare it with that of pioneers such as ChatGPT, Claude, Mistral, and Gemini. \u003C\u002Fp>\u003Ch3 id=\"why-these-models\">Why these models? \u003Ca class=\"markdownit-header-anchor\" href=\"#why-these-models\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Well, ChatGPT, Claude, Mistral, and Gemini were all released before 2024, gained widespread recognition, and were praised for their capabilities. They are also the most popular LLMs. On the other hand, the three Chinese LLMs all emerged in 2025 promising an AI revolution. Our translation quality comparison will be like a battle between the old and the new, between the West and the East, traditional and innovative. \u003C\u002Fp>\u003Cp>Who will win?\u003C\u002Fp>\u003Ch3 id=\"a-note-about-deepl\">A note about DeepL \u003Ca class=\"markdownit-header-anchor\" href=\"#a-note-about-deepl\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.deepl.com\u002Fes\u002Ftranslator\">DeepL\u003C\u002Fa> has been praised by language experts for the quality of its translation results and has recently \u003Ca href=\"https:\u002F\u002Ffortune.com\u002F2024\u002F07\u002F18\u002Fdeepl-long-a-leader-in-translation-tech-finally-embraces-llms\u002F\">integrated their newest models with a LLM\u003C\u002Fa>. However, \u003Cstrong>we decided to exclude it from this analysis\u003C\u002Fstrong> since it doesn't allow us to feed it with a prompt, nor upload a TM or a style guide. The resulting translations would be punished on the selected indicators (consistency with TM and style guide, and partially also for contextual appropriateness). In addition, DeepL doesn't support Croatian, which is one of the two languages chosen for the test.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg\" class=\"kg-image\" alt loading=\"lazy\" width=\"2000\" height=\"1373\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw2400\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 2400w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch2 id=\"the-great-llm-translation-battle\">⚔️ The great LLM translation battle \u003Ca class=\"markdownit-header-anchor\" href=\"#the-great-llm-translation-battle\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>Battles without rules equal chaos. To make sure our translation quality comparison doesn’t spiral into confusion, we’ll use the following indicators:\u003C\u002Fp>\u003Cp>1️⃣ \u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>2️⃣ \u003Cstrong>Rendering variables in the translated strings\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>3️⃣ \u003Cstrong>Consistency with the style guide\u003C\u002Fstrong> (e.g., gender neutral, engaging)\u003C\u002Fp>\u003Cp>4️⃣ \u003Cstrong>Contextual appropriateness\u003C\u002Fstrong> (e.g., does it fit the mobile app context? Is it right for the target users?)\u003C\u002Fp>\u003Cp>5️⃣ \u003Cstrong>Accuracy\u003C\u002Fstrong> (how closely the translated text matches the meaning of the source text)\u003C\u002Fp>\u003Cp>6️⃣ \u003Cstrong>Fluency\u003C\u002Fstrong> (whether the translated text reads naturally in the target language)\u003C\u002Fp>\u003Cp>All these indicators \u003Cstrong>will be assigned on a scale from 1 to 5 to an English-Polish 🇵🇱 translation of mobile app strings\u003C\u002Fstrong>. We’ll compare the score for this language pair with the translation of the same strings \u003Cstrong>from English into Croatian 🇭🇷\u003C\u002Fstrong>, which is \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Flow-resource-languages\">a low-resource language\u003C\u002Fa> (Polish is considered medium-source). \u003C\u002Fp>\u003Cp>Each LLM will be asked to provide \u003Cstrong>a context-specific, gender-neutral translation\u003C\u002Fstrong>, based on a style guide and translation memory uploaded as a TMX or TXT file. The instructions will also contain the type of results we’re looking for, such as natural, engaging translation consistent with the uploaded documents.\u003C\u002Fp>\u003Cp>In each test, we'll use \u003Cstrong>the same set of 10 strings from an educational app \u003C\u002Fstrong>of varied lengths and complexity:\u003C\u002Fp>\u003Col>\u003Cli>\u003Cem>Tap here to start your learning adventure!\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>You are competing against [User].\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Settings have been successfully updated.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Swipe left to see more options or tap More to open a full menu.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>You have %s new notifications.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Tip of the day: Switch your phone's language setting to the language you're learning to boost daily immersion and accelerate your learning process.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem> You've earned a badge for your streak of %s days! Keep up the great work to unlock more rewards.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Your pronunciation score has improved by s% since last month! Well done!\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Do you really want to delete your account? This action cannot be undone.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Your subscription has expired. Renew now to continue enjoying all the great courses.\u003C\u002Fem>\u003C\u002Fli>\u003C\u002Fol>\u003Cp>🧠 The translation memory contains \u003Cstrong>two 100% matches and two 70% matches\u003C\u002Fstrong>, both for Polish and Croatian.\u003C\u002Fp>\u003Cp>📓 The style guide briefly \u003Cstrong>explains the type of the mobile app\u003C\u002Fstrong> (language learning), \u003Cstrong>describes the target audience\u003C\u002Fstrong> (adults), \u003Cstrong>and the desired tone \u003C\u002Fstrong>(engaging and motivational). It also stresses that translation should be gender-neutral and use the correct terminology for app-related words.\u003C\u002Fp>\u003Cp>💬 Finally, each model will be presented with the same prompt:\u003C\u002Fp>\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cem>Act as a professional English-Polish (English-Croatian) translator specializing in the translation of mobile apps and educational content. I will provide you with 10 strings from a language learning app for adults. As a reference, I will upload a TMX file with a translation memory, and a DOC file with a style guide. Translate the strings based on the reference files, making sure the translation is engaging, consistent, contextually appropriate, and gender-neutral.\u003C\u002Fem>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cblockquote>\u003Cstrong>\u003Cem>Disclaimer: \u003C\u002Fem>\u003C\u002Fstrong>\u003Cem>The translation quality assessments and comparisons presented in this article are based on a limited set of tests perfomed 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>\u003Ch3 id=\"chatgpt-version-gpt-4o\">ChatGPT, version GPT-4o \u003Ca class=\"markdownit-header-anchor\" href=\"#chatgpt-version-gpt-4o\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fchatgpt.com\">ChatGPT\u003C\u002Fa> struggled with some app-related terms for Polish translations and \u003Cstrong>failed to provide engaging gender-neutral translations\u003C\u002Fstrong>. For example, it offered alternative gender verb forms (\u003Cem>Zdobyłeś\u002Faś\u003C\u002Fem>), which was not only incorrectly written (the correct form is\u003Cem> Zdobyłeś(-łaś)\u003C\u002Fem>) but also isn’t a typical wording in mobile apps. In such cases, the translation would be usually rendered with the verb in the present form (\u003Cem>Zdobywasz\u003C\u002Fem>), which is gender-neutral, instead of the past form,, which requires a different form for each gender. \u003C\u002Fp>\u003Cp>Some strings were translated quite literally (\u003Cem>codzienne zanurzenie\u003C\u002Fem> for \u003Cem>daily immersion\u003C\u002Fem>), \u003Cstrong>the model didn’t respect the translation memory\u003C\u002Fstrong> for the 100% matches and wasn’t consistent with the 70% matches. The strings with variables were quite problematic too: “You are competing against [User]” and “You have %s new notifications” were rendered incorrectly. In the first sentence, the variable was translated, and the second reads well only for one plural form.\u003C\u002Fp>\u003Cp>In Croatian, the model also translated the variable (“User”) but did well with preserving the segments from the translation memory.\u003C\u002Fp>\u003Cp>All in all, \u003Cstrong>the translation quality wasn’t bad, however, it requires thorough editing\u003C\u002Fstrong>, especially when it comes to consistency and fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1104\" height=\"1481\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FChatGPT-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FChatGPT-PL.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-PL.JPG 1104w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in ChatGPT.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"805\" height=\"731\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FChatGPT-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-HR.JPG 805w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in ChatGPT.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"claude-version-sonnet-35\">Claude, version Sonnet 3.5 \u003Ca class=\"markdownit-header-anchor\" href=\"#claude-version-sonnet-35\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>Since \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fclaude.ai\">Claude\u003C\u002Fa> doesn’t recognize TMX files\u003C\u002Fstrong>, the TM for this model was uploaded as a TXT file, and the prompt was adapted accordingly. In Polish, this model used different terminology for “Tap” compared to ChatGPT, which is considered “more correct”. \u003Cstrong>The translation had a more natural flow and was more engaging compared to ChatGPT, although the strings were not consistent \u003C\u002Fstrong>with the translation memory. Gender neutrality wasn’t the model’s strong point either.\u003C\u002Fp>\u003Cp>Both in Croatian and Polish, the first variable (“User”) was rendered flawlessly, but the strings with variables for multiple plurals turned out to be problematic again.\u003C\u002Fp>\u003Cp>\u003Cstrong>In both languages, Claude added notes on the translation approach at the end\u003C\u002Fstrong> of the output, which is a handy way to sum up all performed processes.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"793\" height=\"1058\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FClaude-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-PL.JPG 793w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Claude.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"798\" height=\"1500\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FClaude-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-HR.JPG 798w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Claude.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"mistral\">Mistral \u003Ca class=\"markdownit-header-anchor\" href=\"#mistral\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>This time, both TM and the style guide were uploaded to the tool as TXT files, as \u003Cstrong>the model doesn’t support TMX or DOC uploads\u003C\u002Fstrong>. The prompts were tweaked accordingly.\u003C\u002Fp>\u003Cp>Overall, \u003Cstrong>Polish translation was more natural and engaging\u003C\u002Fstrong> compared to the previous two models. \u003Cstrong>However, \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Fmistral.ai\">\u003Cstrong>Mistral\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong> struggled with variables\u003C\u002Fstrong> and didn’t stick to the translation memory. Instead of gender-neutral strings, the model chose the male verb forms, failing to adhere to the style guide. Some phrases were translated too literally (e.g., in strings 3, 6, and 9).\u003C\u002Fp>\u003Cp>Surprisingly, the model failed to preserve the variable “User” in Polish but retained it correctly in the Croatian translation. Strings with variables for multiple plural forms were again the weak points.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"829\" height=\"1301\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FMistral-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-PL.JPG 829w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Mistral.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\">\u003Cfigcaption>Croatian translation in Mistral.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"gemini-version-20-flash\">Gemini, version 2.0 Flash \u003Ca class=\"markdownit-header-anchor\" href=\"#gemini-version-20-flash\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fgemini.google.com\u002F\">Gemini\u003C\u002Fa> supports only image upload\u003C\u002Fstrong>, so both the TM and style guide were fed to the model by copying and pasting the content directly into the chat window.\u003C\u002Fp>\u003Cp>In Polish, the model not only used the strings from the TM but also commented on the instances where it copied a string from the translation memory (see strings 3 and 8). \u003Cstrong>The translations had a decent flow with very few literal phrases, and the terminology was correct\u003C\u002Fstrong> as well.\u003C\u002Fp>\u003Cp>Regarding gender neutrality, the model tried its best to stay impartial, but the passive voice in string 7 came out as a bit too awkward. A better choice would be to use the present tense, where verbs for both genders have the same forms.\u003C\u002Fp>\u003Cp>The TM entries were used correctly in Croatian, although the model could do better in terms of fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"847\" height=\"955\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FGemini-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-PL.JPG 847w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Gemini.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"838\" height=\"988\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FGemini-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-HR.JPG 838w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Gemini.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"deepseek-version-3\">DeepSeek, version 3 \u003Ca class=\"markdownit-header-anchor\" href=\"#deepseek-version-3\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>In DeepSeek, the TM was again uploaded as a TXT, as \u003Cstrong>the model doesn’t read TMX files\u003C\u002Fstrong>. The style guide remained in the DOC format.\u003C\u002Fp>\u003Cp>Overall, the Polish translation sounded more natural and more engaging compared to all the LLMs mentioned above. \u003Cstrong>There were fewer literal translations, the terminology was correct, and the model applied 100% matches from the TM\u003C\u002Fstrong>. Although DeepSeek rendered correctly the variable in string 2, it struggled with string 5 (the translation wasn’t correct for all plural forms), and \u003Cstrong>failed to follow the gender-neutrality rule.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>The model provided handy comments, explaining where TM was used, and displayed an encouraging summary at the end of the process.\u003C\u002Fp>\u003Cp>In Croatian, the model was average in terms of accuracy and fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1064\" height=\"1555\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 1064w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"962\" height=\"1342\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-HR.JPG 962w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"qwen25-max\">Qwen2.5-Max \u003Ca class=\"markdownit-header-anchor\" href=\"#qwen25-max\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>\u003Cstrong>This model allows for only one file upload in a single conversation\u003C\u002Fstrong>, hence the content of the translation memory and style guide were pasted into the chat window.\u003C\u002Fp>\u003Cp>In Polish, there were no surprises when it came to gender neutrality: it was all wrong again (e.g. string 7). \u003Cstrong>Variables were problematic\u003C\u002Fstrong>, too (e.g. string 2), and some phrases sounded too literal. However, the model used the TM matches, followed the style guide quite well, and used the correct terminology. \u003Cstrong>Overall, the translation quality was good\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Variables were the main issue in Croatian (string 2.), and the translation didn’t have a natural flow.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"992\" height=\"816\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FQwen-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-PL.JPG 992w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Qwen.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1066\" height=\"400\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FQwen-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FQwen-HR.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-HR.JPG 1066w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Qwen.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"doubao\">Doubao \u003Ca class=\"markdownit-header-anchor\" href=\"#doubao\">🔗\u003C\u002Fa>\u003C\u002Fh3>\u003Cp>There are two ways to use this model: struggle with the Chinese user interface or head straight to \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002F\">Hugging Face\u003C\u002Fa>, a global AI community. Should you choose the first option: don’t worry, your prompt can remain in English and the tool will reply in English too. For the rest, get ready to rely on your intuition 😊 Alternatively, turn to Google Translate or… an LLM of your choice.\u003C\u002Fp>\u003Cp>\u003Cstrong>The model doesn’t support TMX files\u003C\u002Fstrong>, so the TM was again uploaded as a TXT.\u003C\u002Fp>\u003Cp>Overall, \u003Cstrong>the quality of the Polish translation wasn’t spectacular\u003C\u002Fstrong>: issues with variables (string 2), lack of gender neutrality, and a lot of literal translations. Plus, the matches from the TM weren’t applied. On top of that, \u003Cstrong>there were three grammar mistakes \u003C\u002Fstrong>(strings 4 and 6) – an uncommon phenomenon in the other models tested above, \u003Cstrong>and terminology wasn’t correct \u003C\u002Fstrong>(e.g. in string no 10).\u003C\u002Fp>\u003Cp>Croatian results were disappointing too, although finally the “User” variable in string 2. remained untouched.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"863\" height=\"779\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDoubau-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-PL.JPG 863w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Doubao.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"905\" height=\"657\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDoubau-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-HR.JPG 905w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Doubao.\u003C\u002Ffigcaption>\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>\tThis is how each LLM scores for each indicator and language combination:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Rendering variables in the translated strings\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\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>\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>DeepSeek\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>22\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Gemini\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>22\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Qwen\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>21\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>ChatGPT\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Claude\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Mistral\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>19\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Doubao\u003C\u002Ftd>\n\u003Ctd>1\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>12\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 1\u003C\u002Fstrong>. Test results in Polish.\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>Rendering  variables  in the translated  strings\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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\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\">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\">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\">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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Mistral\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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">2\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Doubao\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">2\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\">2\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">1\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>. Test results in Croatian.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cp>\u003Cstrong>There’s no clear winner of our tests\u003C\u002Fstrong>. DeepSeek, Gemini, and Qwen lead the ranking for the Polish translation. In Croatian, the podium belongs to Claude, Gemini, and DeepSeek, all scoring the same number of points.\u003C\u002Fp>\u003Cp>Our great translation battle proved one thing:\u003Cstrong> the East matched the West, the New shook hands with the Old\u003C\u002Fstrong>. The Chinese AI models provided quality comparable to Gemini, Claude, and ChatGPT. \u003Cstrong>In both languages, Doubao ended at the bottom\u003C\u002Fstrong> and scored lower than Qwen and DeepSeek, but this shouldn’t come as a surprise, as the LLM from ByteDance was trained with the main focus on Chinese language processing.\u003C\u002Fp>\u003Cp>In general, \u003Cstrong>the LLMs in our test delivered accurate translations, although not always fluent\u003C\u002Fstrong>. Variables appeared to be problematic to some extent for nearly each LLM, while the TM and style guide was followed by most models, with some deviations in the Polish text. The context in both languages was preserved quite well, although \u003Cstrong>terminology wasn’t always top-notch\u003C\u002Fstrong>. \u003Cstrong>Preserving gender neutrality\u003C\u002Fstrong> (as part of the style guide) \u003Cstrong>seemed to be the major issue\u003C\u002Fstrong> in all LLMs.\u003C\u002Fp>\u003Cblockquote>The general quality of the translations in our tests was good, although not always fluent. Variables and gender neutrality were the most problematic elements. Context was preserved quite well for both languages, but the terminology wasn't always accurate\u003C\u002Fblockquote>\u003Cp>Translation quality depends, of course, on many factors: the source text type, language combination, prompt, and reference files. You might run a similar test with a legal translation from Baha to Swahili and receive completely different results. However, \u003Cstrong>in our scenario, DeepSeek matches or outperforms most LLM giants\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Finally, although LLMs usually struggle with low-resource languages, \u003Cstrong>in our test scenario the Croatian translations of mobile app strings were quite decent\u003C\u002Fstrong>. This surprisingly good result could be attributed to the ample context (translation memory, style guide), language pair, text type, or all these factors.\u003C\u002Fp>\u003Ch2 id=\"wheres-the-golden-middle\">🤔 Where’s the golden middle? \u003Ca class=\"markdownit-header-anchor\" href=\"#wheres-the-golden-middle\">🔗\u003C\u002Fa>\u003C\u002Fh2>\u003Cp>What do these results tell us?\u003C\u002Fp>\u003Cp>It’s simple.\u003C\u002Fp>\u003Cp>Don’t rely solely on LLMs, no matter what scenario. \u003Cstrong>Mixed approaches work best in most situations\u003C\u002Fstrong>, so if you decide to employ an LLM for your translation and localization tasks, remember to add the human touch. That’s how you can ensure your content is fluent, engaging, and tailored to your target audience.\u003C\u002Fp>\u003Cp>If you’re ready for an effective mix of human and machine translation, take a look at \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fmachine-translation\u002F\">our MT and AI features\u003C\u002Fa>, and head to the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fcontinuous-localization-team\u002F\">Continuous Localization Team\u003C\u002Fa> to cut your time to access the global market and focus on what matters the most: your product.\u003C\u002Fp>\u003Cp>\u003C\u002Fp>",{"id":61,"status":5,"created_on":1969,"modified_on":1970,"icon":1971,"header":1972,"description":1973,"button_label":1974,"link":1975},"2022-03-17T12:23:03.000Z","2024-01-18T13:15:16.000Z","app","Try Localazy today","Start localizing your project today with our TMS! Enjoy localization on autopilot and order professional translation services, all from the same spot.","Register","register",{"slug":1860,"id":1853,"uuid":1977,"title":1859,"html":1978,"comment_id":1853,"feature_image":1863,"featured":163,"visibility":1979,"email_recipient_filter":1980,"created_at":1981,"updated_at":1861,"published_at":1862,"custom_excerpt":1966,"codeinjection_head":10,"codeinjection_foot":10,"custom_template":10,"canonical_url":10,"authors":1982,"tags":1988,"primary_author":2038,"primary_tag":2039,"url":2040,"excerpt":1966,"reading_time":71,"access":163,"send_email_when_published":162,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"email_subject":10,"frontmatter":10,"dictionary":1872,"cta":1968,"plainTags":1864},"e387d287-31c4-4686-8fcf-47c2206fdd1f","\u003Cp>To LLM or not to LLM?\u003C\u002Fp>\u003Cp>And more importantly: \u003Cstrong>which LLM?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>If you can’t make your head around the recent changes in the Large Language Models (LLMs) scene, fear not. You’re not alone.\u003C\u002Fp>\u003Cp>With over 60 large language models (LLMs) \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FList_of_large_language_models\">available on the market\u003C\u002Fa>, it’s easy to get lost in the labyrinth of choices. The scene gets more crowded month after month, as new models enter the game. ☔️ No wonder users around the globe keep pondering whether these new additions can beat the well-known giants.\u003C\u002Fp>\u003Cp>If these users happen to work in the translation and localization industry, the first question that comes to mind is: \u003Cstrong>Can I successfully use these new LLMs for translation tasks?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>You’ll find the answer in our comprehensive test below.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%9B%9D-new-kids-on-the-block\">🛝 New kids on the block\u003C\u002Fh2>\u003Cp>Earlier this year, \u003Ca href=\"https:\u002F\u002Fdaily.dev\u002Fblog\u002Fdeepseek-everything-you-need-to-know-about-this-new-llm-in-one-place\">\u003Cstrong>DeepSeek\u003C\u002Fstrong> made it to the headlines with a splash\u003C\u002Fa>. However, that’s not the only LLM that has recently launched. There are dozens of other newcomers, too.\u003C\u002Fp>\u003Cp>While \u003Ca href=\"https:\u002F\u002Fchat.deepseek.com\u002Fsign_in\">DeepSeek\u003C\u002Fa> \u003Cstrong>promises high performance, open-source accessibility, high efficiency, and low computational costs, it raises significant data privacy concerns\u003C\u002Fstrong>. For starters, it stores user information on servers located in China and fails to guarantee that EU users' rights under GDPR will be met. But that’s not all. The company collects various types of user data, including text inputs, prompts, uploaded files, chat history, device information, and even keystroke patterns. If that doesn’t sound like the scenes from the \u003Cem>Black Mirror\u003C\u002Fem> series coming to life, I don’t know what else does.\u003C\u002Fp>\u003Cp>As if these weren’t enough, \u003Cstrong>DeepSeek tends to be highly biased\u003C\u002Fstrong>. Prompt it to give you information on any topic censored by the Chinese government, and you’ll find yourself in a loop of diversions. 👀  Cultural Revolution in China? It’s beyond the tool’s scope. Who owns the Spratly Islands? Let’s switch topics.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-censorship.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1042\" height=\"954\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-censorship.JPG 1042w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Censorship in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cp>Questions about Chinese matters are not the only issues that DeepSeek likes to cover with silence. \u003Ca href=\"https:\u002F\u002Fwww.euronews.com\u002Fnext\u002F2025\u002F01\u002F31\u002Fharmful-and-toxic-output-deepseek-has-major-security-and-safety-gaps-study-warns\">A study by Enkrypt AI\u003C\u002Fa> found that 83% of bias tests on DeepSeek-R1 resulted in discriminatory output, with biases detected across race, gender, health, and religion. Whatever you plan to do with the tool, \u003Cstrong>make sure to double-check the facts\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.doubao.com\u002Fchat\u002F\">\u003Cstrong>Doubao\u003C\u002Fstrong>\u003C\u002Fa>, another Chinese LLM released in January 2025 by ByteDance (the creators of TikTok), shows a similar behavior when it comes to biases and censorship. However, \u003Cstrong>it promises performance that outshines GPT-4o or Claude\u003C\u002Fstrong> in areas such as knowledge retention, coding, reasoning, and Chinese language processing. It also \u003Ca href=\"https:\u002F\u002Fmedium.com\u002Ftowards-agi\u002Fbytedance-just-dropped-doubao-1-5-pro-the-ai-model-thats-quietly-outsmarting-everyone-b9ebc523558d\">scores higher in terms of operational efficiency\u003C\u002Fa>, using merely USD 0.11 per million tokens for input and USD 0.013 for output. \u003Cstrong>The industry giants have on average 70% higher costs\u003C\u002Fstrong>. 📈\u003C\u002Fp>\u003Cp>Cost-effectiveness is the secret weapon of yet another Chinese LLM. \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fchat.qwen.ai\">Qwen\u003C\u002Fa>\u003C\u002Fstrong> was published by Alibaba in 2023 and received its most powerful version, Qwen2.5-Max, in January this year. This model supports multilingual tasks across more than 29 languages and promises \u003Cstrong>excellent long-form text generation, outstanding coding proficiency, and mathematical reasoning\u003C\u002Fstrong>. It’s also fast and efficient. In terms of speed, \u003Ca href=\"https:\u002F\u002Fwww.amitysolutions.com\u002Fblog\u002Fqwen-2-5-ai-breakthrough-all-records\">Qwen matches GPT-4o and outperforms Gemini 1.5\u003C\u002Fa>.\u003C\u002Fp>\u003Cblockquote>Doubao shines in operational efficiency, with considerably lower costs than the LLM giants. Qwen scores high in data privacy, in contrast to DeepSeek, which raises security concerns and tends to be highly biased\u003C\u002Fblockquote>\u003Cp>Unlike DeepSeek, \u003Cstrong>Qwen scores high in data privacy \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Fwww.tomsguide.com\u002Fai\u002Fi-tested-deepseek-vs-qwen-2-5-with-7-prompts-heres-the-winner\">\u003Cstrong>and seems to be less biased\u003C\u002Fstrong>\u003C\u002Fa>. It correctly answers questions about Chinese or international history and shows fewer biases in other areas. 🧑‍🏫 Another good news is that Qwen aligns with GDPR and other global privacy standards.\u003C\u002Fp>\u003Cp>If it all sounds too good to be true, here’s a reason to temper your expectations: Qwen2.5-Max also has its downsides. It’s not an open-source LLM and doesn’t provide transparent reasoning behind its responses, as \u003Ca href=\"https:\u002F\u002Fwww.webelight.com\u002Fblog\u002Fdeepseek-cyber-attack-a-wake-up-call-for-the-AI-industry\" rel=\"noopener noreferrer nofollow\">DeepSeek R1\u003C\u002Fa> or OpenAI’s o1 do. That’s not necessarily a deal-breaker, but something to consider depending on your needs for transparency and adaptability.\u003C\u002Fp>\u003Cp>With so many options, \u003Cstrong>choosing the right LLM for translation sounds like an impossible mission\u003C\u002Fstrong>. It’s tough to decide which model will help you break language barriers and facilitate all those complex localization and internationalization processes.\u003C\u002Fp>\u003Cblockquote>🎙️ To what extent are AI tools useful in localization and translation? \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fblog\u002Fs02-ep-01-bridging-the-gap-podcast\">Listen to our conversation with Dialect’s CEO Jiří Šebek\u003C\u002Fa>  for an insider take on this topic\u003C\u002Fblockquote>\u003Ch3 id=\"llm-giants-or-the-shiny-new-chinese-toys\">LLM giants or the shiny new Chinese toys?\u003C\u002Fh3>\u003Cp>Let’s put the models to the test. \u003Cstrong>We’ll analyze the translation quality\u003C\u002Fstrong> \u003Cstrong>delivered by the new models\u003C\u002Fstrong> and compare it with that of pioneers such as ChatGPT, Claude, Mistral, and Gemini. \u003C\u002Fp>\u003Ch3 id=\"why-these-models\">Why these models?\u003C\u002Fh3>\u003Cp>Well, ChatGPT, Claude, Mistral, and Gemini were all released before 2024, gained widespread recognition, and were praised for their capabilities. They are also the most popular LLMs. On the other hand, the three Chinese LLMs all emerged in 2025 promising an AI revolution. Our translation quality comparison will be like a battle between the old and the new, between the West and the East, traditional and innovative. \u003C\u002Fp>\u003Cp>Who will win?\u003C\u002Fp>\u003Ch3 id=\"a-note-about-deepl\">A note about DeepL\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.deepl.com\u002Fes\u002Ftranslator\">DeepL\u003C\u002Fa> has been praised by language experts for the quality of its translation results and has recently \u003Ca href=\"https:\u002F\u002Ffortune.com\u002F2024\u002F07\u002F18\u002Fdeepl-long-a-leader-in-translation-tech-finally-embraces-llms\u002F\">integrated their newest models with a LLM\u003C\u002Fa>. However, \u003Cstrong>we decided to exclude it from this analysis\u003C\u002Fstrong> since it doesn't allow us to feed it with a prompt, nor upload a TM or a style guide. The resulting translations would be punished on the selected indicators (consistency with TM and style guide, and partially also for contextual appropriateness). In addition, DeepL doesn't support Croatian, which is one of the two languages chosen for the test.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg\" class=\"kg-image\" alt loading=\"lazy\" width=\"2000\" height=\"1373\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1600\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 1600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw2400\u002F2025\u002F03\u002FAdobeStock_552284771.jpeg 2400w\" sizes=\"(min-width: 720px) 720px\">\u003C\u002Ffigure>\u003Ch2 id=\"%E2%9A%94%EF%B8%8F-the-great-llm-translation-battle\">⚔️ The great LLM translation battle\u003C\u002Fh2>\u003Cp>Battles without rules equal chaos. To make sure our translation quality comparison doesn’t spiral into confusion, we’ll use the following indicators:\u003C\u002Fp>\u003Cp>1️⃣ \u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>2️⃣ \u003Cstrong>Rendering variables in the translated strings\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>3️⃣ \u003Cstrong>Consistency with the style guide\u003C\u002Fstrong> (e.g., gender neutral, engaging)\u003C\u002Fp>\u003Cp>4️⃣ \u003Cstrong>Contextual appropriateness\u003C\u002Fstrong> (e.g., does it fit the mobile app context? Is it right for the target users?)\u003C\u002Fp>\u003Cp>5️⃣ \u003Cstrong>Accuracy\u003C\u002Fstrong> (how closely the translated text matches the meaning of the source text)\u003C\u002Fp>\u003Cp>6️⃣ \u003Cstrong>Fluency\u003C\u002Fstrong> (whether the translated text reads naturally in the target language)\u003C\u002Fp>\u003Cp>All these indicators \u003Cstrong>will be assigned on a scale from 1 to 5 to an English-Polish 🇵🇱 translation of mobile app strings\u003C\u002Fstrong>. We’ll compare the score for this language pair with the translation of the same strings \u003Cstrong>from English into Croatian 🇭🇷\u003C\u002Fstrong>, which is \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Fdictionary\u002Flow-resource-languages\">a low-resource language\u003C\u002Fa> (Polish is considered medium-source). \u003C\u002Fp>\u003Cp>Each LLM will be asked to provide \u003Cstrong>a context-specific, gender-neutral translation\u003C\u002Fstrong>, based on a style guide and translation memory uploaded as a TMX or TXT file. The instructions will also contain the type of results we’re looking for, such as natural, engaging translation consistent with the uploaded documents.\u003C\u002Fp>\u003Cp>In each test, we'll use \u003Cstrong>the same set of 10 strings from an educational app \u003C\u002Fstrong>of varied lengths and complexity:\u003C\u002Fp>\u003Col>\u003Cli>\u003Cem>Tap here to start your learning adventure!\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>You are competing against [User].\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Settings have been successfully updated.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Swipe left to see more options or tap More to open a full menu.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>You have %s new notifications.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Tip of the day: Switch your phone's language setting to the language you're learning to boost daily immersion and accelerate your learning process.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem> You've earned a badge for your streak of %s days! Keep up the great work to unlock more rewards.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Your pronunciation score has improved by s% since last month! Well done!\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Do you really want to delete your account? This action cannot be undone.\u003C\u002Fem>\u003C\u002Fli>\u003Cli>\u003Cem>Your subscription has expired. Renew now to continue enjoying all the great courses.\u003C\u002Fem>\u003C\u002Fli>\u003C\u002Fol>\u003Cp>🧠 The translation memory contains \u003Cstrong>two 100% matches and two 70% matches\u003C\u002Fstrong>, both for Polish and Croatian.\u003C\u002Fp>\u003Cp>📓 The style guide briefly \u003Cstrong>explains the type of the mobile app\u003C\u002Fstrong> (language learning), \u003Cstrong>describes the target audience\u003C\u002Fstrong> (adults), \u003Cstrong>and the desired tone \u003C\u002Fstrong>(engaging and motivational). It also stresses that translation should be gender-neutral and use the correct terminology for app-related words.\u003C\u002Fp>\u003Cp>💬 Finally, each model will be presented with the same prompt:\u003C\u002Fp>\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cem>Act as a professional English-Polish (English-Croatian) translator specializing in the translation of mobile apps and educational content. I will provide you with 10 strings from a language learning app for adults. As a reference, I will upload a TMX file with a translation memory, and a DOC file with a style guide. Translate the strings based on the reference files, making sure the translation is engaging, consistent, contextually appropriate, and gender-neutral.\u003C\u002Fem>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cblockquote>\u003Cstrong>\u003Cem>Disclaimer: \u003C\u002Fem>\u003C\u002Fstrong>\u003Cem>The translation quality assessments and comparisons presented in this article are based on a limited set of tests perfomed 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>\u003Ch3 id=\"chatgpt-version-gpt-4o\">ChatGPT, version GPT-4o\u003C\u002Fh3>\u003Cp>\u003Ca href=\"https:\u002F\u002Fchatgpt.com\">ChatGPT\u003C\u002Fa> struggled with some app-related terms for Polish translations and \u003Cstrong>failed to provide engaging gender-neutral translations\u003C\u002Fstrong>. For example, it offered alternative gender verb forms (\u003Cem>Zdobyłeś\u002Faś\u003C\u002Fem>), which was not only incorrectly written (the correct form is\u003Cem> Zdobyłeś(-łaś)\u003C\u002Fem>) but also isn’t a typical wording in mobile apps. In such cases, the translation would be usually rendered with the verb in the present form (\u003Cem>Zdobywasz\u003C\u002Fem>), which is gender-neutral, instead of the past form,, which requires a different form for each gender. \u003C\u002Fp>\u003Cp>Some strings were translated quite literally (\u003Cem>codzienne zanurzenie\u003C\u002Fem> for \u003Cem>daily immersion\u003C\u002Fem>), \u003Cstrong>the model didn’t respect the translation memory\u003C\u002Fstrong> for the 100% matches and wasn’t consistent with the 70% matches. The strings with variables were quite problematic too: “You are competing against [User]” and “You have %s new notifications” were rendered incorrectly. In the first sentence, the variable was translated, and the second reads well only for one plural form.\u003C\u002Fp>\u003Cp>In Croatian, the model also translated the variable (“User”) but did well with preserving the segments from the translation memory.\u003C\u002Fp>\u003Cp>All in all, \u003Cstrong>the translation quality wasn’t bad, however, it requires thorough editing\u003C\u002Fstrong>, especially when it comes to consistency and fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1104\" height=\"1481\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FChatGPT-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FChatGPT-PL.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-PL.JPG 1104w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in ChatGPT.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"805\" height=\"731\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FChatGPT-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FChatGPT-HR.JPG 805w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in ChatGPT.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"claude-version-sonnet-35\">Claude, version Sonnet 3.5\u003C\u002Fh3>\u003Cp>Since \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fclaude.ai\">Claude\u003C\u002Fa> doesn’t recognize TMX files\u003C\u002Fstrong>, the TM for this model was uploaded as a TXT file, and the prompt was adapted accordingly. In Polish, this model used different terminology for “Tap” compared to ChatGPT, which is considered “more correct”. \u003Cstrong>The translation had a more natural flow and was more engaging compared to ChatGPT, although the strings were not consistent \u003C\u002Fstrong>with the translation memory. Gender neutrality wasn’t the model’s strong point either.\u003C\u002Fp>\u003Cp>Both in Croatian and Polish, the first variable (“User”) was rendered flawlessly, but the strings with variables for multiple plurals turned out to be problematic again.\u003C\u002Fp>\u003Cp>\u003Cstrong>In both languages, Claude added notes on the translation approach at the end\u003C\u002Fstrong> of the output, which is a handy way to sum up all performed processes.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"793\" height=\"1058\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FClaude-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-PL.JPG 793w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Claude.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"798\" height=\"1500\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FClaude-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FClaude-HR.JPG 798w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Claude.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"mistral\">Mistral\u003C\u002Fh3>\u003Cp>This time, both TM and the style guide were uploaded to the tool as TXT files, as \u003Cstrong>the model doesn’t support TMX or DOC uploads\u003C\u002Fstrong>. The prompts were tweaked accordingly.\u003C\u002Fp>\u003Cp>Overall, \u003Cstrong>Polish translation was more natural and engaging\u003C\u002Fstrong> compared to the previous two models. \u003Cstrong>However, \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Fmistral.ai\">\u003Cstrong>Mistral\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong> struggled with variables\u003C\u002Fstrong> and didn’t stick to the translation memory. Instead of gender-neutral strings, the model chose the male verb forms, failing to adhere to the style guide. Some phrases were translated too literally (e.g., in strings 3, 6, and 9).\u003C\u002Fp>\u003Cp>Surprisingly, the model failed to preserve the variable “User” in Polish but retained it correctly in the Croatian translation. Strings with variables for multiple plural forms were again the weak points.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"829\" height=\"1301\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FMistral-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-PL.JPG 829w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Mistral.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FMistral-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\">\u003Cfigcaption>Croatian translation in Mistral.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"gemini-version-20-flash\">Gemini, version 2.0 Flash\u003C\u002Fh3>\u003Cp>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fgemini.google.com\u002F\">Gemini\u003C\u002Fa> supports only image upload\u003C\u002Fstrong>, so both the TM and style guide were fed to the model by copying and pasting the content directly into the chat window.\u003C\u002Fp>\u003Cp>In Polish, the model not only used the strings from the TM but also commented on the instances where it copied a string from the translation memory (see strings 3 and 8). \u003Cstrong>The translations had a decent flow with very few literal phrases, and the terminology was correct\u003C\u002Fstrong> as well.\u003C\u002Fp>\u003Cp>Regarding gender neutrality, the model tried its best to stay impartial, but the passive voice in string 7 came out as a bit too awkward. A better choice would be to use the present tense, where verbs for both genders have the same forms.\u003C\u002Fp>\u003Cp>The TM entries were used correctly in Croatian, although the model could do better in terms of fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"847\" height=\"955\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FGemini-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-PL.JPG 847w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Gemini.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"838\" height=\"988\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FGemini-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FGemini-HR.JPG 838w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Gemini.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"deepseek-version-3\">DeepSeek, version 3\u003C\u002Fh3>\u003Cp>In DeepSeek, the TM was again uploaded as a TXT, as \u003Cstrong>the model doesn’t read TMX files\u003C\u002Fstrong>. The style guide remained in the DOC format.\u003C\u002Fp>\u003Cp>Overall, the Polish translation sounded more natural and more engaging compared to all the LLMs mentioned above. \u003Cstrong>There were fewer literal translations, the terminology was correct, and the model applied 100% matches from the TM\u003C\u002Fstrong>. Although DeepSeek rendered correctly the variable in string 2, it struggled with string 5 (the translation wasn’t correct for all plural forms), and \u003Cstrong>failed to follow the gender-neutrality rule.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>The model provided handy comments, explaining where TM was used, and displayed an encouraging summary at the end of the process.\u003C\u002Fp>\u003Cp>In Croatian, the model was average in terms of accuracy and fluency.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1064\" height=\"1555\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-PL-1.JPG 1064w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"962\" height=\"1342\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDeepSeek-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDeepSeek-HR.JPG 962w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in DeepSeek.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"qwen25-max\">Qwen2.5-Max\u003C\u002Fh3>\u003Cp>\u003Cstrong>This model allows for only one file upload in a single conversation\u003C\u002Fstrong>, hence the content of the translation memory and style guide were pasted into the chat window.\u003C\u002Fp>\u003Cp>In Polish, there were no surprises when it came to gender neutrality: it was all wrong again (e.g. string 7). \u003Cstrong>Variables were problematic\u003C\u002Fstrong>, too (e.g. string 2), and some phrases sounded too literal. However, the model used the TM matches, followed the style guide quite well, and used the correct terminology. \u003Cstrong>Overall, the translation quality was good\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Variables were the main issue in Croatian (string 2.), and the translation didn’t have a natural flow.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"992\" height=\"816\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FQwen-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-PL.JPG 992w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Qwen.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"1066\" height=\"400\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FQwen-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw1000\u002F2025\u002F02\u002FQwen-HR.JPG 1000w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FQwen-HR.JPG 1066w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Qwen.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch3 id=\"doubao\">Doubao\u003C\u002Fh3>\u003Cp>There are two ways to use this model: struggle with the Chinese user interface or head straight to \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002F\">Hugging Face\u003C\u002Fa>, a global AI community. Should you choose the first option: don’t worry, your prompt can remain in English and the tool will reply in English too. For the rest, get ready to rely on your intuition 😊 Alternatively, turn to Google Translate or… an LLM of your choice.\u003C\u002Fp>\u003Cp>\u003Cstrong>The model doesn’t support TMX files\u003C\u002Fstrong>, so the TM was again uploaded as a TXT.\u003C\u002Fp>\u003Cp>Overall, \u003Cstrong>the quality of the Polish translation wasn’t spectacular\u003C\u002Fstrong>: issues with variables (string 2), lack of gender neutrality, and a lot of literal translations. Plus, the matches from the TM weren’t applied. On top of that, \u003Cstrong>there were three grammar mistakes \u003C\u002Fstrong>(strings 4 and 6) – an uncommon phenomenon in the other models tested above, \u003Cstrong>and terminology wasn’t correct \u003C\u002Fstrong>(e.g. in string no 10).\u003C\u002Fp>\u003Cp>Croatian results were disappointing too, although finally the “User” variable in string 2. remained untouched.\u003C\u002Fp>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-PL.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"863\" height=\"779\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDoubau-PL.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-PL.JPG 863w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Polish translation in Doubao.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Cfigure class=\"kg-card kg-image-card kg-card-hascaption\">\u003Cimg src=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-HR.JPG\" class=\"kg-image\" alt loading=\"lazy\" width=\"905\" height=\"657\" srcset=\"https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002Fsize\u002Fw600\u002F2025\u002F02\u002FDoubau-HR.JPG 600w, https:\u002F\u002Fghost.localazy.com\u002Fcontent\u002Fimages\u002F2025\u002F02\u002FDoubau-HR.JPG 905w\" sizes=\"(min-width: 720px) 720px\">\u003Cfigcaption>Croatian translation in Doubao.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch2 id=\"%F0%9F%8F%85-what%E2%80%99s-the-llm-leaderboard\">🏅 What’s the LLM leaderboard?\u003C\u002Fh2>\u003Cp>\tThis is how each LLM scores for each indicator and language combination:\u003C\u002Fp>\u003C!--kg-card-begin: html-->\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>\u003Cstrong>LLMs\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Consistency with translation memories\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Rendering variables in the translated strings\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\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>\u003Cstrong>Accuracy\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Fluency\u003C\u002Fstrong>\u003C\u002Fth>\n\u003Cth>\u003Cstrong>Total Score\u003C\u002Fstrong>\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>DeepSeek\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>22\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Gemini\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>22\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Qwen\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>21\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>ChatGPT\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Claude\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Mistral\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">5\u003C\u002Ftd>\n\u003Ctd>5\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd>19\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Doubao\u003C\u002Ftd>\n\u003Ctd>1\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>3\u003C\u002Ftd>\n\u003Ctd style=\"text-align: center\">2\u003C\u002Ftd>\n\u003Ctd>4\u003C\u002Ftd>\n\u003Ctd>2\u003C\u002Ftd>\n\u003Ctd>12\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\u003C!--kg-card-end: html-->\u003Cdiv class=\"kg-card kg-callout-card kg-callout-card-grey\">\u003Cdiv class=\"kg-callout-text\">\u003Cstrong>Table 1\u003C\u002Fstrong>. Test results in Polish.\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>Rendering  variables  in the translated  strings\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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">26\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\">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\">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\">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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Mistral\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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">2\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\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\">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\">3\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">24\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd style=\"text-align:center\">Doubao\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">2\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\">2\u003C\u002Ftd>\n\u003Ctd style=\"text-align:center\">1\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>. Test results in Croatian.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cp>\u003Cstrong>There’s no clear winner of our tests\u003C\u002Fstrong>. DeepSeek, Gemini, and Qwen lead the ranking for the Polish translation. In Croatian, the podium belongs to Claude, Gemini, and DeepSeek, all scoring the same number of points.\u003C\u002Fp>\u003Cp>Our great translation battle proved one thing:\u003Cstrong> the East matched the West, the New shook hands with the Old\u003C\u002Fstrong>. The Chinese AI models provided quality comparable to Gemini, Claude, and ChatGPT. \u003Cstrong>In both languages, Doubao ended at the bottom\u003C\u002Fstrong> and scored lower than Qwen and DeepSeek, but this shouldn’t come as a surprise, as the LLM from ByteDance was trained with the main focus on Chinese language processing.\u003C\u002Fp>\u003Cp>In general, \u003Cstrong>the LLMs in our test delivered accurate translations, although not always fluent\u003C\u002Fstrong>. Variables appeared to be problematic to some extent for nearly each LLM, while the TM and style guide was followed by most models, with some deviations in the Polish text. The context in both languages was preserved quite well, although \u003Cstrong>terminology wasn’t always top-notch\u003C\u002Fstrong>. \u003Cstrong>Preserving gender neutrality\u003C\u002Fstrong> (as part of the style guide) \u003Cstrong>seemed to be the major issue\u003C\u002Fstrong> in all LLMs.\u003C\u002Fp>\u003Cblockquote>The general quality of the translations in our tests was good, although not always fluent. Variables and gender neutrality were the most problematic elements. Context was preserved quite well for both languages, but the terminology wasn't always accurate\u003C\u002Fblockquote>\u003Cp>Translation quality depends, of course, on many factors: the source text type, language combination, prompt, and reference files. You might run a similar test with a legal translation from Baha to Swahili and receive completely different results. However, \u003Cstrong>in our scenario, DeepSeek matches or outperforms most LLM giants\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Finally, although LLMs usually struggle with low-resource languages, \u003Cstrong>in our test scenario the Croatian translations of mobile app strings were quite decent\u003C\u002Fstrong>. This surprisingly good result could be attributed to the ample context (translation memory, style guide), language pair, text type, or all these factors.\u003C\u002Fp>\u003Ch2 id=\"%F0%9F%A4%94-where%E2%80%99s-the-golden-middle\">🤔 Where’s the golden middle?\u003C\u002Fh2>\u003Cp>What do these results tell us?\u003C\u002Fp>\u003Cp>It’s simple.\u003C\u002Fp>\u003Cp>Don’t rely solely on LLMs, no matter what scenario. \u003Cstrong>Mixed approaches work best in most situations\u003C\u002Fstrong>, so if you decide to employ an LLM for your translation and localization tasks, remember to add the human touch. That’s how you can ensure your content is fluent, engaging, and tailored to your target audience.\u003C\u002Fp>\u003Cp>If you’re ready for an effective mix of human and machine translation, take a look at \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fmachine-translation\u002F\">our MT and AI features\u003C\u002Fa>, and head to the \u003Ca href=\"https:\u002F\u002Flocalazy.com\u002Ffeatures\u002Fcontinuous-localization-team\u002F\">Continuous Localization Team\u003C\u002Fa> to cut your time to access the global market and focus on what matters the most: your product.\u003C\u002Fp>\u003Cp>\u003C\u002Fp>","public","none","2025-02-25T18:04:27.000+01:00",[1983],{"id":1855,"name":1856,"slug":1857,"profile_image":1858,"cover_image":10,"bio":1984,"website":1985,"location":1986,"facebook":10,"twitter":10,"meta_title":10,"meta_description":10,"url":1987},"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, 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Large Language Model (LLM)","term-185","https:\u002F\u002Fghost.localazy.com\u002Ftag\u002Fterm-185\u002F",{"id":2004,"name":2005,"slug":2006,"description":1900,"feature_image":10,"visibility":1979,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"codeinjection_head":10,"codeinjection_foot":10,"canonical_url":10,"accent_color":10,"url":2007},"66abc1ee34f1ce00011a2606","[DICT] Neural Machine Translation (NMT)","term-189","https:\u002F\u002Fghost.localazy.com\u002Ftag\u002Fterm-189\u002F",{"id":2009,"name":2010,"slug":2011,"description":1921,"feature_image":10,"visibility":1979,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"codeinjection_head":10,"codeinjection_foot":10,"canonical_url":10,"accent_color":10,"url":2012},"60a37fc7cf7b6a0001d1f12a","[DICT] Machine translation (MT)","term-13","https:\u002F\u002Fghost.localazy.com\u002Ftag\u002Fterm-13\u002F",{"id":2014,"name":2015,"slug":2016,"description":1937,"feature_image":10,"visibility":1979,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"codeinjection_head":10,"codeinjection_foot":10,"canonical_url":10,"accent_color":10,"url":2017},"668f731834f1ce00011a1c05","[DICT] AI-powered translation","term-169","https:\u002F\u002Fghost.localazy.com\u002Ftag\u002Fterm-169\u002F",{"id":2019,"name":2020,"slug":2021,"description":1944,"feature_image":10,"visibility":1979,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"codeinjection_head":10,"codeinjection_foot":10,"canonical_url":10,"accent_color":10,"url":2022},"60a37fc7cf7b6a0001d1f124","[DICT] Translation Memory (TM)","term-7","https:\u002F\u002Fghost.localazy.com\u002Ftag\u002Fterm-7\u002F",{"id":2024,"name":2025,"slug":2026,"description":1957,"feature_image":10,"visibility":1979,"og_image":10,"og_title":10,"og_description":10,"twitter_image":10,"twitter_title":10,"twitter_description":10,"meta_title":10,"meta_description":10,"codeinjection_head":10,"codeinjection_foot":10,"canonical_url":10,"accent_color":10,"url":2027},"60a37fc7cf7b6a0001d1f13e","[DICT] TMX - Translation Memory eXchange 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