Best For
Best LLM for Non-English Languages in 2026
Most LLMs are trained primarily on English. The gap between English and other language quality varies enormously by model — and by which language you're using. Here is what to use depending on your language.
Updated February 2026
What actually matters for multilingual
Before we get to the pick — the criteria that separate good from bad here:
Training data quality for your language — Most LLMs are trained primarily on English text. Performance drops significantly for lower-resource languages. Tier 1 (Chinese, Spanish, French, German, Japanese) is strong across most models; Tier 3 languages (many African and Southeast Asian languages) are unreliable in most models.
Script and character accuracy — Non-Latin scripts — Arabic, CJK characters, Cyrillic, Devanagari — are where models most commonly make character-level errors. Test your specific script before committing to a model for production.
Translation naturalness — There's a wide gap between accurate translation and natural translation. Does the output read like something a native speaker would write, or is it technically correct but stilted?
Instruction-following in the target language — Can it follow detailed instructions given in the target language, or does it default to English reasoning and translate the result? True multilingual capability means thinking in the language, not just translating.
Our pick
Gemini 3.1 Pro leads the MMMLU multilingual benchmark with 92.6% — the highest published score across all models. Google's training data infrastructure gives Gemini a structural advantage in language diversity. For most non-English tasks, Gemini 3.1 Pro is the technically strongest choice. Available in 40+ languages with consistent quality across European, Asian, and Middle Eastern languages.
Pricing: API at $2/$12 per 1M tokens. Google One AI Premium ($20/month) for Gemini App access.
Also consider
Gemini 3 Pro scores 91.8% on MMMLU — essentially the same as 3.1 Pro and free at gemini.google.com. For most people doing non-English work, the free Gemini 3 Pro is the practical choice. Same broad language coverage as 3.1 Pro without the subscription cost.
Free at gemini.google.com. API at $2/$12 per 1M tokens.
Full review →Mistral is a French company and Mistral Large 3 has exceptional quality in French, Spanish, Italian, German, and Portuguese. For European languages specifically, it often outperforms Google and OpenAI models on nuance, idiom, and cultural context. The best choice for EU-based applications or content targeting European audiences.
$0.50/$1.50 per 1M tokens via Mistral API. Free limited tier at chat.mistral.ai.
Full review →Qwen 3 235B is trained by Alibaba on substantial Chinese data and leads on Chinese-language benchmarks — outperforming GPT and Claude on formal Chinese, technical Chinese, and Chinese-language reasoning. If your use case centers on Chinese, Japanese, or Korean, Qwen is the strongest open-source option. MIT license means you can self-host for zero data sharing.
$0.20/$0.88 per 1M tokens on Alibaba Cloud. MIT license — self-host for free.
Full review →Bottom line
For most non-English use cases: Gemini 3 Pro free at gemini.google.com (highest MMMLU score, free, no card required). For European-language depth (French, Spanish, Italian, German): Mistral Large 3 at $0.50/1M. For Chinese, Japanese, or Korean: Qwen 3 235B (best open-source) or DeepSeek V3.2 (best managed API). Note that all models degrade somewhat in lower-resource languages — always test on your specific language before committing to a model for production use.