Models

Eight of the translation APIs are LLM-based services, and six of the LLMs are open-weight models whose weights anyone can download. We aim to include every commercially available MT engine and LLM, and we use each one through a paid account, as a customer would. We use Intento Language Hub for uniform API access to MT engines and LLMs, with deterministic request routing, request batching, quota and account management.

The list holds the models available on 16 July 2026, when we froze it. The last ones added before the freeze were Claude Fable 5, Claude Sonnet 5 and GPT-5.6. Meta Llama 4 Maverick joined on 1 September as the only exception, and it runs on the FP8 build its inference provider serves. We also benchmark GPT-4.1, which is still widely deployed in production, next to the GPT-5 models, so a reader can decide on evidence whether to migrate from it.

We set up API access to 64 models, and seven of them did not return translations of our test texts, so they are not in the results: Amazon Nova 2 Pro and Nova 2 Lite, EuroLLM-22B, Sarvam-Translate, T-tact AN-ZIN, TREBE and Yaraku. Five vendors give access to their models only after approval: Mirai, Kakao and GPT-NL declined, and Rozetta and Tilmoch did not reply. Translated launched a new version of Lara after the model freeze, so the results describe Lara 2.

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The median ratio is ~2.3 times as many errors in the best MT engine translation.

The best MT engine is DeepL Next Gen in 49 languages and Translated Lara 2 in 23. The gap is smallest in Romanian, European Portuguese and Bosnian, and only the DeepL Next Gen solutions in German and Romanian meet general translation quality (no critical error, at most three major errors and at most 20 weighted errors per 1,000 words).

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Translations from OpenAI GPT-5.6 Sol, Anthropic Claude Fable 5, OpenAI GPT-5.5, GPT-5.6 Terra and Google Gemini 3.1 Pro Preview have fewer errors than the median of the other shortlisted LLMs in 95% to 100% of the languages where we shortlisted them. For OpenAI GPT-4.1, Google Gemma 4 and Mistral Large 3, the share is under 15%. The number of errors depends on the language as well, but the order of the models changes little from one language to another.

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The LLM share among the top performers was 89% in 2025 and 55% in 2024, using the earlier definition described below.

The other 1% come from DeepL Next Gen, an LLM-based MT engine, in German and Romanian. We shortlist at least three real-time solutions in each language, nearly all of them MT engines, so MT engines make up 23% of the one-step solutions we assess with Intento LQA.

In the previous edition, a top performer was a solution whose quality score was statistically tied with the best one in its language pair, so the earlier shares come from a different way of counting.

No critical error occurs in 56% of MT engine solutions and 89% of LLM solutions.

Major and critical errors make up 49% of the weighted errors in MT engine translations and 30% in LLM translations. The gap holds at the same number of errors. With 35 to 50 errors per 1,000 words, 42% of the MT engine solutions have no critical error, against 80% of the LLM solutions.

Translations from Google Translation LLM, Amazon Translate and ModernMT Enterprise Edition have a critical error in every language where we shortlisted them.

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They have ~1.6 times as many errors as LLM translations in the same language, at the median. The LLM-based MT engines on our shortlists are DeepL Next Gen, Translated Lara 2, Google Translation LLM and Widn AI Tower Sugarloaf, and the other MT engines are mostly Microsoft Translator.

1% of the LLM-based MT engine solutions meet the formatting requirement, against 84% of the LLM solutions.

Vendor documentation does not reliably say which languages a model supports. LLMs have no language list, and when we tested the language lists of MT vendors, they were wrong in both directions: we did not get a translation in some listed languages, and got translations in some unlisted ones.

From 21 of the 57 models, in 94 of the 100 languages, we got text in the wrong language, garbage or a near-empty output, a translation cut off part-way, text in the wrong script, or text far shorter than the source. We remove such outputs before scoring quality, because a quality score for a text that is not a translation means nothing. A model supports a language only when we got a translation in it from the model, and that is how we determine language support for every model in the report.

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We count a language as supported by an LLM when the translation of our four texts has at most one critical error, no hallucinated segment and at most 60 errors per 1,000 words, weighted by severity. A language where we get no translation from the LLM at all counts as not supported.

Translations from Anthropic Claude Fable 5 and OpenAI GPT-5.6 Sol meet these criteria in every language where they are on the shortlist, and from Google Gemini 3.1 Pro Preview in all but Burmese. Translations from OpenAI gpt-oss-120b and xAI Grok 4.20 (non-reasoning) meet them in 6 of 12 and 3 of 8 languages.

Translations from Fable 5 meet the much higher bar of general translation quality (no critical error, at most three major errors and at most 20 weighted errors per 1,000 words) in 33 of the 60 languages. We assess an LLM in a language only where it is on our shortlist.

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