Agentic Coding Benchmark

SWE-bench Verified

SWE-bench Verified evaluates LLM-based agents on real-world GitHub issue resolution from popular Python repositories. Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

42

What is SWE-bench Verified?

SWE-bench Verified is a benchmark in which a model has to fix real software bugs in popular open-source Python projects. The model gets a GitHub issue and the repository, and must change the code itself until the automated tests pass. The tasks were reviewed by OpenAI engineers, so a task is only in the set when it is clearly reproducible and solvable.

What does it test?

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

  • finding the right file(s) in a large real codebase
  • writing a fix that solves the described issue
  • making sure the project's existing tests still pass
  • working autonomously with terminal and file-editing tools

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.

Frequently asked questions

What is the SWE-bench Verified benchmark?

SWE-bench Verified is a benchmark in which a model has to fix real software bugs in popular open-source Python projects. The model gets a GitHub issue and the repository, and must change the code itself until the automated tests pass. The tasks were reviewed by OpenAI engineers, so a task is only in the set when it is clearly reproducible and solvable.

What does SWE-bench Verified test?

SWE-bench Verified mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: finding the right file(s) in a large real codebase; writing a fix that solves the described issue; making sure the project's existing tests still pass.

How is the SWE-bench Verified 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-bench Verified data and scores?

The official source is https://www.swebench.com/. 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-bench-verified/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 42 models tested

Reset
# Model Score Date Source Verified
1 DeepSeek-V4-Pro
DeepSeek
80.6%
26.06.2026 Report Verified
2 MiniMax-M3
MiniMaxAI
80.5%
11.06.2026 MiniMax-M3 official evaluation metadata Verified
3 Ornith-1.5-35B-A3B
Ornith AI
79%
19.08.2026 Report Verified
4 DeepSeek-V4-Flash
DeepSeek
79%
26.06.2026 Report Verified
5 Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
78.7%
— Report Unverified
6 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
77.2%
22.04.2026 Qwen Team (official model card) Verified
7 Kimi K2.5
Moonshot AI
76.8%
23.08.2026 Report Verified
8 Ripple-65B Preview
Ricursive Intelligence
76.5%
— Report Verified
9 Muse-Glimmer-30B
Meta Superintelligence Lab
76%
01.08.2026 Meta Superintelligence Lab (official model card); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 72 Verified
10 Kimi K2.6
Moonshot AI
75.7%
04.06.2026 Report Unverified
11 Ornith-1.0-35B
DeepReinforce AI
75.6%
25.06.2026 DeepReinforce Team (official model card) Verified
12 Ornith-1.0-35B-A3B
Ornith AI
75.6%
19.08.2026 Report Unverified
13 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
75%
16.04.2026 Qwen Team (official model card - comparison) Unverified
14 Xing4.0-29B-A4B
China Telecom AI
75%
16.09.2026 Report Verified
15 Qwen3.8-27B
Qwen Team (Alibaba Cloud)
74.6%
21.10.2026 Report Unverified
16 DeepSeek-V4-Pro
DeepSeek
74.5%
04.06.2026 Report Unverified
17 Flint-40B-A2.5B
Recursive
74.2%
— HF card https://huggingface.co/RecursiveSI/flint - mini-swe-agent harness, temp 1.0, top-p 0.95, top-k 20; average pass@1 over at least five attempts; measured by Recursive Verified
18 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
73.4%
16.04.2026 Qwen Team (official model card); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 63.2 Verified
19 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
72%
01.02.2026 Report Verified
20 Raon-1 mini
KRAFTON AI
70.8%
21.10.2026 Report Verified
21 NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
70.7%
04.06.2026 Report Verified
22 Qwen3-Coder-Next
Qwen
70.6%
03.02.2026 Report Verified
23 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
70%
16.04.2026 Qwen Team (official model card - comparison) Unverified
24 Ornith-1.0-9B
DeepReinforce AI
69.4%
25.06.2026 DeepReinforce Team (official model card) Verified
25 Kolibri-1
Aleph Alpha
66.4%
03.10.2026 Report Verified
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What now?

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