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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 9 models tested
| # | 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 |