Multilingual Benchmark

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.

Model scores 9 models tested

Reset
# 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

What now?

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