All evaluations

MMMU-Pro Benchmark Leaderboard

An enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.

MMMU-Pro addresses limitations in the original MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark) through a three-step enhancement process: filtering out questions answerable by text-only models, expanding multiple-choice options from 4 to 10, and introducing a vision-only input format where questions are embedded within screenshots or photos.
The benchmark contains 3,460 questions across six core disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering) and requires models to simultaneously process visual and textual information in a more realistic setting.
Performance results show substantial drops across all tested models compared to the original MMMU, demonstrating the benchmark's effectiveness in exposing current limitations in multimodal AI systems.

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig.

This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly "see" and "read" simultaneously, testing a fundamental human cognitive skill of seamlessly integrating visual and textual information. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future research in multimodal AI.

MMMU-Pro

Gemini 3.1 Pro Preview scores the highest on MMMU-Pro with a score of 82%, followed by GPT-5.5 (medium) with a score of 81%, and GPT-5.5 (high) with a score of 81%

MMMU-Pro Benchmark Leaderboard: Results

Independently benchmarked by Artificial Analysis

MMMU-Pro Benchmark Leaderboard: Token Usage

Tokens used to run the evaluation
Input tokens
Reasoning tokens
Answer tokens

The total number of tokens used to run the evaluation, including input tokens (prompt), reasoning tokens (for reasoning models), and answer tokens (final response).

MMMU-Pro Benchmark Leaderboard: Cost Breakdown

Cost (USD) to run the evaluation
Input cost
Reasoning cost
Answer cost

The cost to run the evaluation, calculated using the model's input and output token pricing and the number of tokens used.

MMMU-Pro Benchmark Leaderboard: Score vs. Release Date

Most attractive region
Alibaba
Amazon
Anthropic
Google
Kimi
Mistral
OpenAI
xAI

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