PolyMATH
PolyMATH - multilingual mathematical reasoning benchmark.
Category
Multilingual
Score type
percent
Scale
relative
higher is better
Models tested
9
What is PolyMATH?
PolyMATH is a benchmark for performance in languages other than English. The official description says: "PolyMATH - multilingual mathematical reasoning benchmark". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
PolyMATH measures performance in languages other than english. In detail, it checks:
- understanding and answering in many different languages
- translation quality between language pairs
- keeping math and reasoning quality when the question is not in English
- treating low-resource languages fairly instead of defaulting to English
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: PolyMATH - multilingual mathematical reasoning benchmark.
Frequently asked questions
What is the PolyMATH benchmark?
PolyMATH is a benchmark for performance in languages other than English. The official description says: 'PolyMATH - multilingual mathematical reasoning benchmark'. Below we describe in simple words what benchmarks of this type generally examine.
What does PolyMATH test?
PolyMATH mainly measures performance in languages other than English. Concretely, it checks: understanding and answering in many different languages; translation quality between language pairs; keeping math and reasoning quality when the question is not in English.
How is the PolyMATH score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Where do I get PolyMATH 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/polymath/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)
|
71.2%
|
01.02.2026 | Report | Unverified |
| 2 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
68.9%
|
01.02.2026 | Report | Verified |
| 3 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
64.4%
|
01.02.2026 | Report | Unverified |
| 4 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
60.1%
|
01.02.2026 | Report | Unverified |
| 5 |
Qwen3-Next-80B-A3B-Thinking
Qwen
|
56.3%
|
10.09.2025 | Report | Verified |
| 6 |
Qwen3-30B-A3B-Thinking-2507
Qwen
|
52.6%
|
31.07.2025 | Report | Verified |
| 7 |
Qwen3.5-4B
Qwen
|
51.1%
|
01.02.2026 | Report | Verified |
| 8 |
Qwen3-30B-A3B
Qwen
|
46.1%
|
29.04.2025 | Report | Verified |
| 9 |
Qwen3-VL-30B-A3B
Qwen
|
44.3%
|
04.10.2025 | Report | Verified |