language benchmark
Global-MMLU-Lite
A lightweight version of Global MMLU benchmark that evaluates language models across multiple languages while addressing cultural and linguistic biases in multilingual evaluation.
Updated Aug 11, 2026
Models14
Model coverage14
MetricScore
EvidenceB
Global-MMLU-Lite Ranking
Higher score ranks better on this benchmark.
| 01 | GO | 89.2% | 100.0% | 14 | C | |
| 02 | GO | 88.6% | 92.3% | 14 | C | |
| 03 | GO | 88.4% | 84.6% | 14 | C | |
| 04 | GO | 81.1% | 76.9% | 14 | C | |
| 05 | GO | 78.2% | 69.2% | 14 | C | |
| 06 | GO | 75.1% | 61.5% | 14 | C | |
| 07 | GO | 69.5% | 53.9% | 14 | C | |
| 08 | GO | 69.1% | 46.1% | 14 | C | |
| 09 | GO | 64.5% | 38.5% | 14 | C | |
| 10 | GO | 64.5% | 30.8% | 14 | C | |
| 11 | GO | 59.0% | 23.1% | 14 | C | |
| 12 | GO | 59.0% | 15.4% | 14 | C | |
| 13 | GO | 54.5% | 7.7% | 14 | C | |
| 14 | GO | 34.2% | 0.0% | 14 | C |
Global-MMLU-Lite Score Distribution
A closer view of the leading scores on this benchmark.
Global-MMLU-Lite Highlights
The leading models and scores on this benchmark.
What is Global-MMLU-Lite?
What Global-MMLU-Lite measures and how its scores work.
A lightweight version of Global MMLU benchmark that evaluates language models across multiple languages while addressing cultural and linguistic biases in multilingual evaluation.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Global-MMLU-Lite
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- global-mmlu-lite|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Global-MMLU-Lite.
Which model scores highest on Global-MMLU-Lite?
Gemini 2.5 Pro Preview 06-05 is currently ranked first with 89.2%.
What does Global-MMLU-Lite measure?
A lightweight version of Global MMLU benchmark that evaluates language models across multiple languages while addressing cultural and linguistic biases in multilingual evaluation.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
14 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.