Agentic Coding Benchmark

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.

Model scores 9 models tested

Reset
# 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

What now?

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