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PolyMath-en

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Interactive timeline showing model performance evolution on PolyMath-en

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PolyMath-en Leaderboard

2 models
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1
Moonshot AI
Moonshot AI
1.0T
11.0T
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About this benchmark

What is 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.

PolyMath-en is a text benchmark evaluating models on math and reasoning tasks. LLM Stats tracks 2 models on this benchmark, scored on a 0–1 scale. The current average is 0.7, with the leader at 0.7.

Compare leaders on the best AI for math and best AI for reasoning leaderboards.

Current leaders

Kimi K2 Instruct from Moonshot AI currently leads the PolyMath-en leaderboard with a score of 0.651 across 2 evaluated AI models.

1Kimi K2 InstructMoonshot AI65.1%
1Kimi K2-Instruct-0905Moonshot AI65.1%

Source paper

Title
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
Authors
Yiming Wang, Pei Zhang, Jialong Tang, Haoran Wei, and 12 others
Published
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.

FAQ

Common questions about the PolyMath-en benchmark and leaderboard.

What is the PolyMath-en benchmark?

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.

What is the PolyMath-en leaderboard?

The PolyMath-en leaderboard ranks 2 AI models based on their performance on this benchmark. Currently, Kimi K2 Instruct by Moonshot AI leads with a score of 0.651. The average score across all models is 0.651.

What is the highest PolyMath-en score?

The highest PolyMath-en score is 0.651, achieved by Kimi K2 Instruct from Moonshot AI.

How many models are evaluated on PolyMath-en?

2 models have been evaluated on the PolyMath-en benchmark, with 0 verified results and 2 self-reported results.

Where can I find the PolyMath-en paper?

The PolyMath-en paper is available at https://arxiv.org/abs/2504.18428. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does PolyMath-en cover?

PolyMath-en is categorized under math and reasoning. The benchmark evaluates text models with multilingual support.

What is the best open-source model on PolyMath-en?

Kimi K2 Instruct by Moonshot AI is the top-ranked open-source model on PolyMath-en, with a score of 0.651 (rank #1).

How recent are the PolyMath-en leaderboard results?

The PolyMath-en leaderboard was last updated in August 2026 and currently includes 2 evaluated models.