ClawBench
Benchmarks are how you separate marketing claims from measured reality. Instead of trusting vendor-reported numbers, benchmarks run the same tasks against every system under a shared methodology and publish the results. For AI product builders picking an agentic extraction or search stack, a trustworthy benchmark is the single best input to the build-vs-buy decision – and a fast way to spot when a category is still too immature to rely on.
✓JS Rendering
✓Structured Output
✓Open Source
✓Self-Hosted Option
Pricing:Free
A rare benchmark run on real production websites rather than sandboxed environments. Five layers of behavioral data – session replay, screenshots, HTTP traffic, reasoning traces, and browser actions – make failure analysis tractable, while a request interceptor blocks irreversible actions like payments and bookings before they fire. Ships as `pip install clawbench-eval` with an interactive leaderboard and trace viewer at claw-bench.com.
The current numbers are sobering: Claude Sonnet 4.6 tops at 33.3%, GLM-5 trails at 24.2%, and no model exceeds 50% in any category. Finance and academic tasks are easier; travel and dev tasks are much harder. If you're building or picking an agentic extraction stack, this is the honest scoreboard to test against.
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ClawBench