reasoning benchmark
Multilingual MMLU Leaderboard
MMLU-ProX is a comprehensive multilingual benchmark covering 29 typologically diverse languages, building upon MMLU-Pro. Each language version consists of 11,829 identical questions enabling direct cross-linguistic comparisons. The benchmark evaluates large language models' reasoning capabilities across linguistic and cultural boundaries through challenging, reasoning-focused questions with 10 answer choices.
Updated Aug 17, 2026
Models5
Model coverage5
MetricScore
EvidenceB
Multilingual MMLU Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score80.7% | Percentile100.0% | Participants5 | EvidenceC | Evaluated |
| Rank02 | ModelMA | Score74.2% | Percentile75.0% | Participants5 | EvidenceC | Evaluated |
| Rank03 | ModelMA | Score70.6% | Percentile50.0% | Participants5 | EvidenceC | Evaluated |
| Rank04 | ModelMA | Score65.2% | Percentile25.0% | Participants5 | EvidenceC | Evaluated |
| Rank05 | ModelMI | Score49.3% | Percentile0.0% | Participants5 | EvidenceC | Evaluated |
Multilingual MMLU Highlights
The leading models and scores on this benchmark.
Multilingual MMLU Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Multilingual MMLU
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis multilingual mmlu AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01OPo3-miniOpenAI
- Score
- 80.7%
- Price
- $1.1 input / $4.4 output per 1M tokens
- Speed
- Up to 115 tok/s via Azure
Strengths
- Ranks #1 of 5 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures Multilingual MMLU, not total model capability
- 02MAMinistral 3 (14B Base 2512)Mistral AI
- Score
- 74.2%
- Price
- $0.10 input / $0.10 output per 1M tokens
- Speed
- Up to 238 tok/s via Mistral AI
Strengths
- Ranks #2 of 5 compared models
- 75th percentile on this benchmark
- C evidence result
Considerations
- This result measures Multilingual MMLU, not total model capability
- 03MAMinistral 3 (8B Base 2512)Mistral AI
- Score
- 70.6%
- Price
- $0.10 input / $0.10 output per 1M tokens
- Speed
- Up to 238 tok/s via Mistral AI
Strengths
- Ranks #3 of 5 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures Multilingual MMLU, not total model capability
- 04MAMinistral 3 (3B Base 2512)Mistral AI
- Score
- 65.2%
- Price
- $0.10 input / $0.10 output per 1M tokens
- Speed
- Up to 238 tok/s via Mistral AI
Strengths
- Ranks #4 of 5 compared models
- 25th percentile on this benchmark
- C evidence result
Considerations
- This result measures Multilingual MMLU, not total model capability
- 05MIPhi 4 MiniMicrosoft
- Score
- 49.3%
- Price
- $0.07 input / $0.30 output per 1M tokens
Strengths
- Ranks #5 of 5 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures Multilingual MMLU, not total model capability
Selection summary
Best AI Models for Multilingual MMLU
o3-mini currently leads Multilingual MMLU with 80.7%. 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 is Multilingual MMLU?
What Multilingual MMLU measures and how its scores work.
MMLU-ProX is a comprehensive multilingual benchmark covering 29 typologically diverse languages, building upon MMLU-Pro. Each language version consists of 11,829 identical questions enabling direct cross-linguistic comparisons. The benchmark evaluates large language models' reasoning capabilities across linguistic and cultural boundaries through challenging, reasoning-focused questions with 10 answer choices.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Multilingual MMLU
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- multilingual-mmlu|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Multilingual MMLU.
Which model scores highest on Multilingual MMLU?
o3-mini is currently ranked first with 80.7%.
What does Multilingual MMLU measure?
MMLU-ProX is a comprehensive multilingual benchmark covering 29 typologically diverse languages, building upon MMLU-Pro. Each language version consists of 11,829 identical questions enabling direct cross-linguistic comparisons. The benchmark evaluates large language models' reasoning capabilities across linguistic and cultural boundaries through challenging, reasoning-focused questions with 10 answer choices.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
5 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.