Vision & Language Benchmark

DynaMath

DynaMath - dynamic mathematical reasoning in visual contexts.

Category

Vision & Language

Score type

percent

Scale

100%

higher is better

Models tested

9

What is DynaMath?

DynaMath is a benchmark for understanding images combined with text. The official description says: "DynaMath - dynamic mathematical reasoning in visual contexts". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

DynaMath 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: DynaMath - dynamic mathematical reasoning in visual contexts.

Frequently asked questions

What is the DynaMath benchmark?

DynaMath is a benchmark for understanding images combined with text. The official description says: 'DynaMath - dynamic mathematical reasoning in visual contexts'. Below we describe in simple words what benchmarks of this type generally examine.

What does DynaMath test?

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

Model scores 9 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
87.7%
22.04.2026 Qwen Team (official model card - comparison) Unverified
2 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
85.9%
01.02.2026 Report Verified
3 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
85.6%
22.04.2026 Qwen Team (official model card) Verified
4 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
85%
01.02.2026 Report Unverified
5 Qwen3.5-4B
Qwen
83.3%
01.02.2026 Report Verified
6 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
82.8%
22.04.2026 Qwen Team (official model card - comparison) Unverified
7 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
82.8%
01.02.2026 Report Unverified
8 Gemma4-31B-it
Google
79.5%
22.04.2026 Qwen Team (official model card - comparison) Unverified
9 LFM3-VL-2B
LiquidAI
79.2%
— Report Verified

What now?

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