Comparison

The Best LLM for Multilingual Tasks in 2026

Which LLM handles multilingual tasks best in 2026? We compare Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.5 on MMMLU, the leading multilingual benchmark.

6 min read

Multilingual capability is increasingly a hard requirement rather than a nice-to-have. Global customer-facing applications, multilingual document processing, and international research pipelines all need models that perform consistently across languages — not just in English.

This post uses MMMLU (Multilingual Massive Multitask Language Understanding), the most widely used multilingual benchmark, to rank the leading models. For a detailed explanation of the benchmark, see what is MMLU.

What MMMLU measures and why it matters

MMMLU extends the original MMLU benchmark — 57 knowledge domains from elementary school math to professional law and medicine — across 57 languages. A model must not only possess domain knowledge but also access and express it accurately in languages spanning different scripts, grammatical structures, and cultural contexts.

This makes MMMLU a strong proxy for how a model will perform on real-world multilingual tasks: customer support in non-English languages, translation-adjacent reasoning tasks, and cross-lingual information retrieval. For the wider framework on interpreting benchmark scores, see the complete guide to LLM benchmarks.

MMMLU scores: the full ranking

Among the models tracked on LLM Boss with available MMMLU data:

Claude Opus 4.8 and Mythos Preview do not currently report MMMLU scores, limiting this comparison to the three models above.

Gemini 3.1 Pro's multilingual advantage

Gemini 3.1 Pro's 92.6% is 1.1 points ahead of Claude Opus 4.7's 91.5% — a small gap at the top of the leaderboard. But its 9.4-point lead over GPT-5.5 (83.2%) is substantial and suggests a meaningful architectural or training advantage for non-English languages.

This aligns with Google's historically strong investment in multilingual training data. Gemini models are trained on data spanning a broader set of languages and scripts than most competitors, and that investment shows clearly on MMMLU. For a full model profile, see the Gemini 3.1 Pro model page.

The GPT-5.5 gap: what it means in practice

GPT-5.5's 83.2% MMMLU score is a significant step down from the top two models. For applications that primarily handle English, this gap is largely irrelevant — GPT-5.5 performs well on English-language benchmarks including AA-LCR (74.3%) and Terminal-Bench (78.2%).

But for multilingual applications, a 9-point gap on MMMLU is hard to overlook. It suggests that GPT-5.5 will make more errors on non-English inputs, especially in lower-resource languages where training data is sparser.

Teams using GPT-5.5 for multilingual workloads should run their own language-specific evaluations rather than relying solely on aggregate MMMLU scores. A model that performs well in Spanish and French may still struggle in Thai or Swahili.

Claude Opus 4.7 vs Gemini 3.1 Pro

The 1.1-point gap between Opus 4.7 and Gemini 3.1 Pro is small enough that it may not be the deciding factor for most teams. The broader comparison between these models should account for:

  • Gemini 3.1 Pro leads on MMMLU (92.6% vs 91.5%) and GPQA Diamond (94.3% vs — not reported for Opus 4.7).
  • Claude Opus 4.8 leads Gemini 3.1 Pro on MCP-Atlas (82.2% vs 78.2%) and OSWorld-Verified (83.4% vs 76.2%).
  • See Claude Opus 4.8 vs Gemini 3.1 Pro for the full side-by-side across all benchmarks.

If multilingual performance is your primary requirement, Gemini 3.1 Pro wins on the available data. If your application spans multiple capability domains, evaluate the full benchmark profile against your specific workload.

Practical recommendations for multilingual deployments

  • Multilingual customer support: Gemini 3.1 Pro is the strongest choice based on MMMLU. Evaluate on the specific language mix your application handles.
  • Cross-lingual document processing: Gemini 3.1 Pro leads, but Claude Opus 4.7 is a close second and may be preferred for its tool-use capabilities in document pipelines.
  • Mixed English + multilingual pipelines: GPT-5.5's English strengths partially offset its multilingual gap. Run language-specific evals before committing to a production choice.

The full MMMLU scores alongside all other benchmarks are visible in the live benchmark comparison table.

Key takeaways

  • Best for multilingual tasks: Gemini 3.1 Pro at 92.6% on MMMLU — leading Claude Opus 4.7 by 1.1 points and GPT-5.5 by 9.4 points.
  • Claude Opus 4.7 (91.5%) is a strong multilingual option, especially if your pipeline also relies on strong tool-use or agentic capability.
  • GPT-5.5 (83.2%) lags significantly on MMMLU — it is not the right default choice for multilingual production applications.
  • Aggregate MMMLU scores mask per-language variance; always run language-specific evaluations for production multilingual systems.
  • For multilingual applications that also require strong reasoning, see the best LLM for reasoning, where GPQA Diamond scores show a different competitive picture.
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