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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 42 models tested
| # | 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 |