MMMU-Pro
MMMU-Pro - enhanced MMMU with harder questions and refined evaluation.
Category
Vision & Language
Score type
percent
Scale
100%
higher is better
Models tested
24
What is MMMU-Pro?
MMMU-Pro is a benchmark for understanding images combined with text. The official description says: "MMMU-Pro - enhanced MMMU with harder questions and refined evaluation". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
MMMU-Pro measures understanding images combined with text. In detail, it checks:
- answering questions about images (figures, photos, charts, memes)
- reading text inside images and connecting it to the question
- visual reasoning such as counting, locating and comparing objects
- combining what is seen with general knowledge
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: MMMU-Pro - enhanced MMMU with harder questions and refined evaluation.
Frequently asked questions
What is the MMMU-Pro benchmark?
MMMU-Pro is a benchmark for understanding images combined with text. The official description says: 'MMMU-Pro - enhanced MMMU with harder questions and refined evaluation'. Below we describe in simple words what benchmarks of this type generally examine.
What does MMMU-Pro test?
MMMU-Pro mainly measures understanding images combined with text. Concretely, it checks: answering questions about images (figures, photos, charts, memes); reading text inside images and connecting it to the question; visual reasoning such as counting, locating and comparing objects.
How is the MMMU-Pro score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get MMMU-Pro data and scores?
The official source is https://artificialanalysis.ai/evaluations/mmmu-pro. 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/mmmu-pro/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.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
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 24 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K3.1 Pro
Moonshot AI
|
84.1%
|
07.11.2026 | Report | Verified |
| 2 |
Kimi K3
Moonshot AI
|
83.4%
|
18.08.2026 | Report | Verified |
| 3 |
DeepSeek-V4.1-Flash-1017
DeepSeek
|
80.6%
|
07.11.2026 | Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro | Unverified |
| 4 |
Kimi K2.6
Moonshot AI
|
79.4%
|
11.06.2026 | MiniMax-M3 model card benchmark table | Unverified |
| 5 |
Kimi K2.5
Moonshot AI
|
78.5%
|
23.08.2026 | Report | Verified |
| 6 |
MiniMax-M3
MiniMaxAI
|
78.1%
|
11.06.2026 | MiniMax-M3 official evaluation metadata | Verified |
| 7 |
Gemma4-31B-it
Google
|
76.9%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 8 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
76.9%
|
01.02.2026 | Report | Verified |
| 9 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
75.8%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 10 |
Bonsai 2 27B — GGUF
Prism ML
|
75.49%
|
17.09.2026 | Report | Verified |
| 11 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
75.3%
|
16.04.2026 | Qwen Team (official model card) | Verified |
| 12 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
75.1%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 13 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
75%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 14 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
74%
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Verified |
| 15 |
Gemma4-26B-A4B
Google
|
73.8%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 16 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
69.3%
|
01.02.2026 | Report | Unverified |
| 17 |
Gemma4-12B
Google
|
69.1%
|
02.07.2026 | Report | Verified |
| 18 |
Qwen3.5-4B
Qwen
|
66.3%
|
01.02.2026 | Report | Verified |
| 19 |
Gemma4-E4B
Google
|
52.6%
|
02.07.2026 | Report | Verified |
| 20 |
Gemma3-27B
Google
|
49.7%
|
02.07.2026 | Report | Unverified |
| 21 |
LFM3-VL-2B
LiquidAI
|
45.7%
|
— | Report | Verified |
| 22 |
Gemma4-E2B
Google
|
44.2%
|
02.07.2026 | Report | Verified |
| 23 |
LFM3-Omni-2B
LiquidAI
|
27.28%
|
— | Report | Verified |
| 24 |
d1-2B
LiquidAI
|
26.4%
|
— | LiquidAI/d1-2B model card (performance table) | Unverified |