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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 15 models tested
| # | 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 |