Claw-Anything: leaderboard
Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate over only narrow slices of that world, limiting context-sensitive reasoning and effective assistance. Existing benchmarks similarly provide only partial user state and therefore fail to capture performance in such a broad
Metric: Pass@1 (self-reported). Source: benchmarklist.com. Saturation forecast: Around July 2027. 9 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.5 | 34.5 | #31 |
| 2 | Claude Opus 4.7 | 31.8 | #49 |
| 3 | GLM-5.1 | 31.7 | #97 |
| 4 | Claude Sonnet 4.5 | 28 | #142 |
| 5 | Kimi K2.6 | 22.8 | #104 |
| 6 | Qwen 3.6 27B | 22.5 | #170 |
| 7 | MiniMax-M2.7 | 13.5 | #258 |
| 8 | Qwen 3.5 27B | 9.8 | #187 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=claw-anything · How It Works · Data refreshed daily, snapshot 2026-10-20.