Agentic Coding Benchmark

SWE-Marathon

SWE-Marathon - long-horizon software engineering benchmark. Evaluated by Abundant AI with 1M context length, max effort level, and 128K maximum output tokens.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

8

What is SWE-Marathon?

SWE-Marathon is a benchmark for how well a model works as an autonomous coding agent. The official description says: "SWE-Marathon - long-horizon software engineering benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

SWE-Marathon measures how well a model works as an autonomous coding agent. In detail, it checks:

  • fixing real, open bugs from real software repositories
  • planning multi-step changes, running commands and editing files by itself
  • passing the project's test suite after its changes
  • handling whole repositories, not just small isolated code snippets

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: SWE-Marathon - long-horizon software engineering benchmark. Evaluated by Abundant AI with 1M context length, max effort level, and 128K maximum output tokens.

Frequently asked questions

What is the SWE-Marathon benchmark?

SWE-Marathon is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'SWE-Marathon - long-horizon software engineering benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does SWE-Marathon test?

SWE-Marathon mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: fixing real, open bugs from real software repositories; planning multi-step changes, running commands and editing files by itself; passing the project's test suite after its changes.

How is the SWE-Marathon score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get SWE-Marathon data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/swe-marathon/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 8 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3.1 Pro
Moonshot AI
51.7%
07.11.2026 Report Verified
2 Kimi K3
Moonshot AI
48.1%
28.08.2026 Report Verified
3 GLM-5.3
Zhipu AI
42.5%
28.08.2026 Report Verified
4 DeepSeek-V4.1-Flash-1017
DeepSeek
40%
07.11.2026 Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro Unverified
5 Hy4 preview
Tencent
31.9%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
6 Qwen3.8-2.4T-A95B
Qwen
31%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) Verified
7 GLM-5.2
Zhipu AI
13%
17.06.2026 Report Verified
8 Hy3
Tencent
5%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified

What now?

You've seen how models score on SWE-Marathon — here's what to do next.