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reasoning benchmark

PaperBench

PaperBench is a benchmark for evaluating AI agents on their ability to replicate research papers. It tests models on complex, multi-step workflows involving code implementation, experimentation, and reproducing scientific results from academic publications.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

PaperBench Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01ACQwen3.8 MaxAlibaba Cloud / Qwen Team93.0%100.0%3CAug 11, 2026
02MAKimi K2.5Moonshot AI63.5%50.0%3CAug 11, 2026
03MIMiniMax M3MiniMax52.6%0.0%3CAug 11, 2026

PaperBench Score Distribution

A closer view of the leading scores on this benchmark.

PaperBench

PaperBench Highlights

The leading models and scores on this benchmark.

Rank #1Qwen3.8 Max93.0%Rank #2Kimi K2.563.5%Rank #3MiniMax M352.6%

What is PaperBench?

What PaperBench measures and how its scores work.

PaperBench is a benchmark for evaluating AI agents on their ability to replicate research papers. It tests models on complex, multi-step workflows involving code implementation, experimentation, and reproducing scientific results from academic publications.

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

Family
PaperBench
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
paperbench|llm-stats-current

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

FAQ

Common questions about PaperBench.

Which model scores highest on PaperBench?

Qwen3.8 Max is currently ranked first with 93.0%.

What does PaperBench measure?

PaperBench is a benchmark for evaluating AI agents on their ability to replicate research papers. It tests models on complex, multi-step workflows involving code implementation, experimentation, and reproducing scientific results from academic publications.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.