language benchmark
French MMLU is the French version of MMLU-Pro, a multilingual benchmark for evaluating language models' cross-lingual reasoning capabilities across 14 diverse domains.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score57.50% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis french mmlu AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Ministral 8B Instruct currently leads French MMLU with 57.50%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What French MMLU measures and how its scores work.
French MMLU is a multilingual benchmark based on MMLU-Pro, covering 14 domains including mathematics, physics, chemistry, law, engineering, psychology, and health.
It measures language models' cross-lingual reasoning capabilities using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about French MMLU.
Ministral 8B Instruct is currently ranked first with 57.50%.
The current leaders are Ministral 8B Instruct (57.50%).
No matched official input price is currently available.
The fastest matched records are Ministral 8B Instruct (0.10 tok/s via Mistral AI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures language models' cross-lingual reasoning capabilities using the Score metric, reported as a ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.