MMMU-Pro

A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.

Gemini 3.5 Flash from Google currently leads the MMMU-Pro leaderboard with a score of 0.836 across 49 evaluated AI models.

GoogleGemini 3.5 Flash leads with 83.6%, followed by OpenAIGPT-5.5 at 83.2% and OpenAIGPT-5.4 at 81.2%.

Progress Over Time

Interactive timeline showing model performance evolution on MMMU-Pro

State-of-the-art frontier
Open
Proprietary

MMMU-Pro Leaderboard

49 models
ContextCostLicense
11.0M$1.50 / $9.00
2
OpenAI
OpenAI
1.1M$5.00 / $30.00
3
OpenAI
OpenAI
1.0M$2.50 / $15.00
31.0M$0.50 / $3.00
5
61.0M$2.50 / $15.00
7
8
Moonshot AI
Moonshot AI
1.0T262K$0.95 / $4.00
9
OpenAI
OpenAI
400K$1.75 / $14.00
10
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
1.0M$0.50 / $3.00
11
Moonshot AI
Moonshot AI
1.0T
12
OpenAI
OpenAI
131.0M$5.00 / $25.00
1431B262K$0.14 / $0.40
14
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
122B262K$0.40 / $3.20
161.0M$0.25 / $1.50
17400K$0.75 / $4.50
18
OpenAI
OpenAI
19400K$5.00 / $30.00
20
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
28B262K$0.60 / $3.60
21200K$3.00 / $15.00
22
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B
23
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B262K$0.25 / $2.00
24
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
27B262K$0.30 / $2.40
2525B262K$0.13 / $0.40
26
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.45 / $3.49
27
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.49
27
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
29400K$0.20 / $1.25
30
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
31
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
32
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
32
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
34
Mistral AI
Mistral AI
119B256K$0.15 / $0.60
35
OpenAI
OpenAI
128K$2.50 / $10.00
36400B
37
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
38
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
39
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $0.60
408B
41
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
72B
42
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
34B
43
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
73B
4490B
455B
466B
47
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
8B
48
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
7B
4911B
Notice missing or incorrect data?

FAQ

Common questions about MMMU-Pro.

What is the MMMU-Pro benchmark?

A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.

What is the MMMU-Pro leaderboard?

The MMMU-Pro leaderboard ranks 49 AI models based on their performance on this benchmark. Currently, Gemini 3.5 Flash by Google leads with a score of 0.836. The average score across all models is 0.664.

What is the highest MMMU-Pro score?

The highest MMMU-Pro score is 0.836, achieved by Gemini 3.5 Flash from Google.

How many models are evaluated on MMMU-Pro?

49 models have been evaluated on the MMMU-Pro benchmark, with 0 verified results and 49 self-reported results.

Where can I find the MMMU-Pro paper?

The MMMU-Pro paper is available at https://arxiv.org/abs/2409.02813. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does MMMU-Pro cover?

MMMU-Pro is categorized under vision, general, multimodal, and reasoning. The benchmark evaluates multimodal models.

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