Agentic Coding Benchmark

WildClawBench

WildClawBench - agentic coding evaluation benchmark. Overall score from internlm/WildClawBench.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

19

What is WildClawBench?

WildClawBench is a benchmark for how well a model works as an autonomous coding agent. The official description says: "WildClawBench - agentic coding evaluation benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

WildClawBench 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: WildClawBench - agentic coding evaluation benchmark. Overall score from internlm/WildClawBench.

Frequently asked questions

What is the WildClawBench benchmark?

WildClawBench is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'WildClawBench - agentic coding evaluation benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does WildClawBench test?

WildClawBench 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 WildClawBench 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 WildClawBench 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/wildclawbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 19 models tested

Reset
# Model Score Date Source Verified
1 Ornith-1.5-35B-A3B
Ornith AI
72.5%
19.08.2026 Report Verified
2 Ornith-1.0-35B-A3B
Ornith AI
69.8%
19.08.2026 Report Unverified
3 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
68.7%
19.08.2026 Report Unverified
4 Kimi K3
Moonshot AI
54.5%
18.08.2026 Report Verified
5 GLM-5.2
Zhipu AI
54.2%
17.06.2026 Report Verified
6 Gemma-4-31B
Google
48.5%
19.08.2026 Report Unverified
7 Muse-Glimmer-30B
Meta Superintelligence Lab
47.6%
01.08.2026 Meta Superintelligence Lab (official model card) Verified
8 Kimi K2.7 Code
Moonshot AI
46.9%
23.08.2026 Report Unverified
9 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
43.2%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified
10 Gemma4-31B-it
Google
37.6%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified
11 MiniCPM5-2B
OpenBMB
23.9%
— Report Verified
12 granite-4.2-3B
IBM
20%
— Report Verified
13 Qwen3.5-4B
Qwen
17%
— Report Verified
14 Gemma4-E4B
Google
14.3%
— Report Verified
15 LFM2.5-2.6B
LiquidAI
10.2%
— Report Verified
16 Qwen3.5-2B
Qwen Team (Alibaba Cloud)
9.2%
— Report Verified
17 Gemma4-E2B
Google
8.9%
— Report Verified
18 Nemotron-3-Nano-4B
NVIDIA
8.9%
— Report Verified
19 LFM2.5-8B-A1B
LiquidAI
4.5%
— Report Verified

What now?

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