SWE Atlas - QnA
SWE Atlas QnA - agentic coding subtask; mini SWE agent harness, temp=1.0, top_p=0.95, 128K context, avg of 5 runs.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
14
What is SWE Atlas - QnA?
SWE Atlas - QnA is a benchmark for how well a model works as an autonomous coding agent. The official description says: "SWE Atlas QnA - agentic coding subtask; mini SWE agent harness, temp=1.0, top_p=0.95, 128K context, avg of 5 runs". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
SWE Atlas - QnA 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 Atlas QnA - agentic coding subtask; mini SWE agent harness, temp=1.0, top_p=0.95, 128K context, avg of 5 runs.
Frequently asked questions
What is the SWE Atlas - QnA benchmark?
SWE Atlas - QnA is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'SWE Atlas QnA - agentic coding subtask; mini SWE agent harness, temp=1.0, top_p=0.95, 128K context, avg of 5 runs'. Below we describe in simple words what benchmarks of this type generally examine.
What does SWE Atlas - QnA test?
SWE Atlas - QnA 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 Atlas - QnA 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 Atlas - QnA 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-atlas-qna/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.
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 14 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Hy4 preview
Tencent
|
64%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 2 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
58%
|
15.10.2026 | Report | Verified |
| 3 |
GLM-5.3
Zhipu AI
|
55.8%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) | Verified |
| 4 |
Qwen3.8-2.4T-A95B
Qwen
|
55.4%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) | Verified |
| 5 |
Nanbeige4.3-33B
Nanbeige
|
50.8%
|
24.10.2026 | Report | Verified |
| 6 |
Ornith-1.5-35B-A3B
Ornith AI
|
39.8%
|
19.08.2026 | Report | Verified |
| 7 |
MiniMax-M3
MiniMaxAI
|
37.9%
|
11.06.2026 | MiniMax-M3 model card benchmark table | Unverified |
| 8 |
Ornith-1.0-35B
DeepReinforce AI
|
37.1%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 9 |
Ornith-1.0-35B-A3B
Ornith AI
|
37.1%
|
19.08.2026 | Report | Unverified |
| 10 |
Kimi K3
Moonshot AI
|
35.2%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) | Verified |
| 11 |
Hy3
Tencent
|
30.8%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 12 |
Ornith-1.0-9B
DeepReinforce AI
|
17.9%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 13 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
15.5%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |
| 14 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
13.2%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |