Vision & Language Benchmark

MMStar

MMStar - comprehensive multimodal benchmark evaluating fine-grained visual reasoning.

Category

Vision & Language

Score type

percent

Scale

100%

higher is better

Models tested

11

What is MMStar?

MMStar is a benchmark for understanding images combined with text. The official description says: "MMStar - comprehensive multimodal benchmark evaluating fine-grained visual reasoning". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

MMStar 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: MMStar - comprehensive multimodal benchmark evaluating fine-grained visual reasoning.

Frequently asked questions

What is the MMStar benchmark?

MMStar is a benchmark for understanding images combined with text. The official description says: 'MMStar - comprehensive multimodal benchmark evaluating fine-grained visual reasoning'. Below we describe in simple words what benchmarks of this type generally examine.

What does MMStar test?

MMStar 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 MMStar 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 MMStar 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/mmstar/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 11 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
82.9%
01.02.2026 Report Verified
2 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
81.9%
01.02.2026 Report Unverified
3 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
81.4%
22.04.2026 Qwen Team (official model card) Verified
4 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
81%
22.04.2026 Qwen Team (official model card - comparison) Unverified
5 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
80.7%
22.04.2026 Qwen Team (official model card - comparison) Unverified
6 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
78.7%
01.02.2026 Report Unverified
7 Qwen3.5-4B
Qwen
78.3%
01.02.2026 Report Verified
8 Gemma4-31B-it
Google
77.3%
22.04.2026 Qwen Team (official model card - comparison) Unverified
9 LFM3-VL-2B
LiquidAI
74.4%
— Report Verified
10 d1-2B
LiquidAI
67.5%
— LiquidAI/d1-2B model card (performance table) Unverified
11 LFM3-Omni-2B
LiquidAI
55.67%
— Report Verified

What now?

You've seen how models score on MMStar — here's what to do next.