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.
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 19 models tested
| # | 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 |