ProgramBench
ProgramBench - agentic coding benchmark evaluated with Claude-Code 2.1.156, 200 instances, temp=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max, 400K context window. Each instance runs in a (4 CPUs, 8 GB RAM) sandbox with internet access disabled.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
9
What is ProgramBench?
ProgramBench is a benchmark for how well a model works as an autonomous coding agent. The official description says: "ProgramBench - agentic coding benchmark evaluated with Claude-Code 2.1.156, 200 instances, temp=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max, 400K context window". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
ProgramBench 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: ProgramBench - agentic coding benchmark evaluated with Claude-Code 2.1.156, 200 instances, temp=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max, 400K context window. Each instance runs in a (4 CPUs, 8 GB RAM) sandbox with internet access disabled.
Frequently asked questions
What is the ProgramBench benchmark?
ProgramBench is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'ProgramBench - agentic coding benchmark evaluated with Claude-Code 2.1.156, 200 instances, temp=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max, 400K context window'. Below we describe in simple words what benchmarks of this type generally examine.
What does ProgramBench test?
ProgramBench 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 ProgramBench 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 ProgramBench 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/programbench/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 9 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
GLM-5.2
Zhipu AI
|
63.7%
|
17.06.2026 | Report | Verified |
| 2 |
DeepSeek-V4-Pro
DeepSeek
|
47.8%
|
17.06.2026 | Report | Unverified |
| 3 |
DeepSeek-V4.1-Flash-1017
DeepSeek
|
29.1%
|
17.10.2026 | Report | Verified |
| 4 |
DeepSeek-V4.1-Flash
DeepSeek
|
20.3%
|
10.09.2026 | Report | Verified |
| 5 |
GLM-5.3
Zhipu AI
|
19%
|
28.08.2026 | Report | Verified |
| 6 |
Kimi K3
Moonshot AI
|
17.5%
|
28.08.2026 | Report | Verified |
| 7 |
Hy4 preview
Tencent
|
17.5%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 8 |
Qwen3.8-2.4T-A95B
Qwen
|
10.5%
|
28.08.2026 | Report | Verified |
| 9 |
Hy3
Tencent
|
3%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |