An end-to-end real-world agent benchmark for OpenClaw-style workflows spanning tool use, planning, execution, and recovery across practical tasks.
BenchLM mirrors the published score view for Claw-Eval. Claude Opus 4.5 leads the public snapshot at 59.6% , followed by Qwen3.6 Plus (58.7%) and GLM-5 (57.7%). BenchLM does not use these results to rank models overall.
Claude Opus 4.5
Anthropic
Qwen3.6 Plus
Alibaba
GLM-5
Z.AI
The published Claw-Eval snapshot is tightly clustered at the top: Claude Opus 4.5 sits at 59.6%, while the third row is only 1.9 points behind. The broader top-10 spread is 11.5 points, so the benchmark still separates strong models even when the leaders cluster.
5 models have been evaluated on Claw-Eval. The benchmark falls in the Agentic category. This category carries a 22% weight in BenchLM.ai's overall scoring system. Claw-Eval is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
Year
2026
Tasks
Real-world agent workflows
Format
End-to-end agent evaluation
Difficulty
Broad real-world agentic execution
Claw-Eval is designed to test whether models can actually complete broad agent workflows instead of only local tool calls. It is useful for comparing agent reliability on realistic multi-step tasks with branching execution paths.
Version
Claw-Eval 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
An end-to-end real-world agent benchmark for OpenClaw-style workflows spanning tool use, planning, execution, and recovery across practical tasks.
Claude Opus 4.5 by Anthropic currently leads with a score of 59.6% on Claw-Eval.
5 AI models have been evaluated on Claw-Eval on BenchLM.
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