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language benchmark

Global-MMLU

A comprehensive multilingual benchmark covering 42 languages that addresses cultural and linguistic biases in evaluation, with improved translation quality and culturally sensitive question subsets.

Updated Aug 11, 2026

Models5
Model coverage5
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

Global-MMLU Ranking

Higher score ranks better on this benchmark.

5 rows
Columns

Show columns

01XIMiMo-V2.5-ProXiaomi83.6%100.0%5CAug 11, 2026
02GOGemma 3n E4B InstructedGoogle60.3%75.0%5CAug 11, 2026
03GOGemma 3n E4B Instructed LiteRT PreviewGoogle60.3%50.0%5CAug 11, 2026
04GOGemma 3n E2B InstructedGoogle55.1%25.0%5CAug 11, 2026
05GOGemma 3n E2B Instructed LiteRT (Preview)Google55.1%0.0%5CAug 11, 2026

Global-MMLU Score Distribution

A closer view of the leading scores on this benchmark.

Global-MMLU

Global-MMLU Highlights

The leading models and scores on this benchmark.

Rank #1MiMo-V2.5-Pro83.6%Rank #2Gemma 3n E4B Instructed60.3%Rank #3Gemma 3n E4B Instructed LiteRT Preview60.3%Rank #4Gemma 3n E2B Instructed55.1%

What is Global-MMLU?

What Global-MMLU measures and how its scores work.

A comprehensive multilingual benchmark covering 42 languages that addresses cultural and linguistic biases in evaluation, with improved translation quality and culturally sensitive question subsets.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Global-MMLU
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
global-mmlu|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Global-MMLU.

Which model scores highest on Global-MMLU?

MiMo-V2.5-Pro is currently ranked first with 83.6%.

What does Global-MMLU measure?

A comprehensive multilingual benchmark covering 42 languages that addresses cultural and linguistic biases in evaluation, with improved translation quality and culturally sensitive question subsets.

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.