PolyMath-en
PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 difficulty levels from easy to hard, ensuring difficulty comprehensiveness, language diversity, and high-quality translation. The benchmark evaluates mathematical reasoning capabilities of large language models across diverse linguistic contexts, making it a highly discriminative multilingual mathematical benchmark.
Kimi K2 Instruct from Moonshot AI currently leads the PolyMath-en leaderboard with a score of 0.651 across 2 evaluated AI models.
What PolyMath-en measures
PolyMath-en is a text benchmark that evaluates large language models on math and reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.7, with the leader reaching 0.7.
Compare leaders on the best AI for math and best AI for reasoning leaderboards.
Publication
- Paper
- PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
- Authors
- Yiming Wang, Pei Zhang, Jialong Tang, Haoran Wei, and 12 others
- Published
- arXiv
- 2504.18428
Abstract
In this paper, we introduce PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation, making it a highly discriminative multilingual mathematical benchmark in the era of reasoning LLMs. We conduct a comprehensive evaluation for advanced LLMs and find that even Qwen-3-235B-A22B-Thinking and Gemini-2.5-pro, achieve only 54.6 and 52.2 benchmark scores, with about 40% accuracy under the highest level From a language perspective, our benchmark reveals several key challenges of LLMs in multilingual reasoning: (1) Reasoning performance varies widely across languages for current LLMs; (2) Input-output language consistency is low in reasoning LLMs and may be correlated with performance; (3) The thinking length differs significantly by language for current LLMs. Additionally, we demonstrate that controlling the output language in the instructions has the potential to affect reasoning performance, especially for some low-resource languages, suggesting a promising direction for improving multilingual capabilities in LLMs.
Kimi K2 Instruct leads with 65.1%, followed by Kimi K2-Instruct-0905 at 65.1%.
Progress Over Time
Interactive timeline showing model performance evolution on PolyMath-en
PolyMath-en Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Moonshot AI | 1.0T | — | — | ||
| 1 | Moonshot AI | 1.0T | — | — |
FAQ
Common questions about PolyMath-en.
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