MMStar

MMStar is an elite vision-indispensable multimodal benchmark comprising 1,500 challenge samples meticulously selected by humans to evaluate 6 core capabilities and 18 detailed axes. The benchmark addresses issues of visual content unnecessity and unintentional data leakage in existing multimodal evaluations.

Qwen3.6 Plus from Alibaba Cloud / Qwen Team currently leads the MMStar leaderboard with a score of 0.833 across 22 evaluated AI models.

Paper

Alibaba Cloud / Qwen TeamQwen3.6 Plus leads with 83.3%, followed by Alibaba Cloud / Qwen TeamQwen3.5-122B-A10B at 82.9% and Alibaba Cloud / Qwen TeamQwen3.5-35B-A3B at 81.9%.

Progress Over Time

Interactive timeline showing model performance evolution on MMStar

State-of-the-art frontier
Open
Proprietary

MMStar Leaderboard

22 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
1.0M$0.50 / $3.00
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
122B262K$0.40 / $3.20
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B262K$0.25 / $2.00
4
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
28B262K$0.60 / $3.60
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
27B262K$0.30 / $2.40
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
7
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.45 / $3.49
8
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.49
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
10
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
11
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
13
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
14
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
15
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
72B
16
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $0.60
17
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
34B
18
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
7B
19
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
8B
20
DeepSeek
DeepSeek
27B
2116B
223B
Notice missing or incorrect data?

FAQ

Common questions about MMStar.

What is the MMStar benchmark?

MMStar is an elite vision-indispensable multimodal benchmark comprising 1,500 challenge samples meticulously selected by humans to evaluate 6 core capabilities and 18 detailed axes. The benchmark addresses issues of visual content unnecessity and unintentional data leakage in existing multimodal evaluations.

What is the MMStar leaderboard?

The MMStar leaderboard ranks 22 AI models based on their performance on this benchmark. Currently, Qwen3.6 Plus by Alibaba Cloud / Qwen Team leads with a score of 0.833. The average score across all models is 0.725.

What is the highest MMStar score?

The highest MMStar score is 0.833, achieved by Qwen3.6 Plus from Alibaba Cloud / Qwen Team.

How many models are evaluated on MMStar?

22 models have been evaluated on the MMStar benchmark, with 0 verified results and 22 self-reported results.

Where can I find the MMStar paper?

The MMStar paper is available at https://arxiv.org/abs/2403.20330. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does MMStar cover?

MMStar is categorized under vision, general, multimodal, and reasoning. The benchmark evaluates multimodal models.

More evaluations to explore

Related benchmarks in the same category

View all vision
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

general
214 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

general
119 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
109 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

general
99 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
90 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

visionmultimodal
75 models