agents benchmark
WildClawBench Leaderboard
WildClawBench is an agentic coding benchmark from InternLM/Claw-Eval that reports overall model performance on real-world tool-using development tasks.
Updated Aug 17, 2026
Models5
Model coverage5
MetricScore
EvidenceB
WildClawBench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelBY | Score62.8% | Percentile100.0% | Participants5 | EvidenceC | Evaluated |
| Rank02 | ModelBY | Score61.7% | Percentile75.0% | Participants5 | EvidenceC | Evaluated |
| Rank03 | ModelTE | Score53.6% | Percentile50.0% | Participants5 | EvidenceC | Evaluated |
| Rank04 | ModelME | Score47.6% | Percentile25.0% | Participants5 | EvidenceC | Evaluated |
| Rank05 | ModelXI | Score43.0% | Percentile0.0% | Participants5 | EvidenceC | Evaluated |
WildClawBench Highlights
The leading models and scores on this benchmark.
WildClawBench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for WildClawBench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis wildclawbench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01BYSeed 2.1 TurboByteDance
- Score
- 62.8%
Strengths
- Ranks #1 of 5 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures WildClawBench, not total model capability
- 02BYSeed 2.1 ProByteDance
- Score
- 61.7%
Strengths
- Ranks #2 of 5 compared models
- 75th percentile on this benchmark
- C evidence result
Considerations
- This result measures WildClawBench, not total model capability
- 03TEHy3Tencent
- Score
- 53.6%
Strengths
- Ranks #3 of 5 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures WildClawBench, not total model capability
- 04MEMuse Glimmer-30BMeta
- Score
- 47.6%
Strengths
- Ranks #4 of 5 compared models
- 25th percentile on this benchmark
- C evidence result
Considerations
- This result measures WildClawBench, not total model capability
- 05XIMiMo-V2.5-ProXiaomi
- Score
- 43.0%
- Price
- $0.43 input / $0.87 output per 1M tokens
- Speed
- Up to 69 tok/s via Novita
Strengths
- Ranks #5 of 5 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures WildClawBench, not total model capability
Selection summary
Best AI Models for WildClawBench
Seed 2.1 Turbo currently leads WildClawBench with 62.8%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is WildClawBench?
What WildClawBench measures and how its scores work.
WildClawBench is an agentic coding benchmark from InternLM/Claw-Eval that reports overall model performance on real-world tool-using development tasks.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- WildClawBench
- Modality
- text
- Primary category
- agents
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- wildclawbench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about WildClawBench.
Which model scores highest on WildClawBench?
Seed 2.1 Turbo is currently ranked first with 62.8%.
What does WildClawBench measure?
WildClawBench is an agentic coding benchmark from InternLM/Claw-Eval that reports overall model performance on real-world tool-using development tasks.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
5 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.