Multilingual Benchmark

MMMLU

MMMLU - Multilingual MMLU benchmark evaluating knowledge across multiple languages.

Category

Multilingual

Score type

percent

Scale

relative

higher is better

Models tested

15

What is MMMLU?

MMMLU is a benchmark for performance in languages other than English. The official description says: "MMMLU - Multilingual MMLU benchmark evaluating knowledge across multiple languages". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

MMMLU measures performance in languages other than english. In detail, it checks:

  • understanding and answering in many different languages
  • translation quality between language pairs
  • keeping math and reasoning quality when the question is not in English
  • treating low-resource languages fairly instead of defaulting to English

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: MMMLU - Multilingual MMLU benchmark evaluating knowledge across multiple languages.

Frequently asked questions

What is the MMMLU benchmark?

MMMLU is a benchmark for performance in languages other than English. The official description says: 'MMMLU - Multilingual MMLU benchmark evaluating knowledge across multiple languages'. Below we describe in simple words what benchmarks of this type generally examine.

What does MMMLU test?

MMMLU mainly measures performance in languages other than English. Concretely, it checks: understanding and answering in many different languages; translation quality between language pairs; keeping math and reasoning quality when the question is not in English.

How is the MMMLU score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Where do I get MMMLU data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/mmmlu/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 15 models tested

Reset
# Model Score Date Source Verified
1 DeepSeek-V4-Pro
DeepSeek
90.3%
26.06.2026 Report Verified
2 DeepSeek-V4-Flash
DeepSeek
88.8%
26.06.2026 Report Verified
3 Gemma4-31B-it
Google
88.4%
02.07.2026 Report Verified
4 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
86.7%
01.02.2026 Report Verified
5 Gemma4-26B-A4B
Google
86.3%
02.07.2026 Report Verified
6 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
85.9%
01.02.2026 Report Unverified
7 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
85.2%
01.02.2026 Report Unverified
8 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
83.4%
01.02.2026 Report Unverified
9 Gemma4-12B
Google
83.4%
02.07.2026 Report Verified
10 GPT-OSS-120B
OpenAI
81.3%
08.08.2025 Report Verified
11 Gemma4-E4B
Google
76.6%
02.07.2026 Report Verified
12 Qwen3.5-4B
Qwen
76.1%
01.02.2026 Report Verified
13 GPT-OSS-20B
OpenAI
75.7%
05.08.2025 Report Verified
14 Gemma3-27B
Google
70.7%
02.07.2026 Report Unverified
15 Gemma4-E2B
Google
67.4%
02.07.2026 Report Verified

What now?

You've seen how models score on MMMLU — here's what to do next.