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math benchmark

PolyMath-en Leaderboard

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.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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PolyMath-en Ranking

Higher score ranks better on this benchmark.

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Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMAKimi K2 InstructMoonshot AIScore65.1%Percentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelMAKimi K2-Instruct-0905Moonshot AIScore65.1%Percentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

PolyMath-en Highlights

The leading models and scores on this benchmark.

Rank #1Kimi K2 Instruct65.1%Rank #2Kimi K2-Instruct-090565.1%

PolyMath-en Score Distribution

A closer view of the leading scores on this benchmark.

PolyMath-en

The Top AI Models for PolyMath-en

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis polymath-en AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    MA
    Kimi K2 InstructMoonshot AI
    Score
    65.1%
    Price
    $0.60 input / $2.5 output per 1M tokens
    Speed
    Up to 45 tok/s via Novita

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures PolyMath-en, not total model capability
  2. 02
    MA
    Kimi K2-Instruct-0905Moonshot AI
    Score
    65.1%
    Price
    $0.60 input / $2.5 output per 1M tokens
    Speed
    Up to 45 tok/s via Novita

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures PolyMath-en, not total model capability

Selection summary

Best AI Models for PolyMath-en

Kimi K2 Instruct currently leads PolyMath-en with 65.1%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1Kimi K2 Instruct65.1% · $0.60 input / $2.5 output per 1M tokensBenchmark rank #2Kimi K2-Instruct-090565.1% · $0.60 input / $2.5 output per 1M tokens

What is PolyMath-en?

What PolyMath-en measures and how its scores work.

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.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
PolyMath-en
Modality
text
Primary category
math
Score direction
higher
LLMBoard eligible
No
Evaluation key
polymath-en|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about PolyMath-en.

Which model scores highest on PolyMath-en?

Kimi K2 Instruct is currently ranked first with 65.1%.

What does PolyMath-en measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.