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agents benchmark

SWE-Marathon

SWE-Marathon is an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services. It measures whether agents can sustain quality across extremely long engineering trajectories.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

SWE-Marathon Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01MAKimi K3Moonshot AI42.0%100.0%3CAug 11, 2026
02XAGrok 4.5xAI29.0%50.0%3CAug 11, 2026
03ZAGLM-5.2Zhipu AI13.0%0.0%3CAug 11, 2026

SWE-Marathon Score Distribution

A closer view of the leading scores on this benchmark.

SWE-Marathon

SWE-Marathon Highlights

The leading models and scores on this benchmark.

Rank #1Kimi K342.0%Rank #2Grok 4.529.0%Rank #3GLM-5.213.0%

What is SWE-Marathon?

What SWE-Marathon measures and how its scores work.

SWE-Marathon is an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services. It measures whether agents can sustain quality across extremely long engineering trajectories.

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

Family
SWE-Marathon
Modality
text
Primary category
agents
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
swe-marathon|llm-stats-current

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

FAQ

Common questions about SWE-Marathon.

Which model scores highest on SWE-Marathon?

Kimi K3 is currently ranked first with 42.0%.

What does SWE-Marathon measure?

SWE-Marathon is an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services. It measures whether agents can sustain quality across extremely long engineering trajectories.

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.