WolfBench: A Five-Metric Framework Redefining Agent Evaluation
morgymcg · x · 2026-08-10
WolfBench introduces a novel evaluation framework for AI agents, arguing that a single average score is insufficient to reflect true model capabilities.
Based on Terminal-Bench 2.0, the leaderboard incorporates five metrics to illustrate performance distribution:
- Ceiling: The proportion of tasks ever solved historically.
- Best-of: The peak score from a single run.
- Average: The mean score.
- Worst-of: The lowest score from a single run.
- Solid: The proportion of tasks consistently solved in every run.
The board tracks the performance of frontier models such as GPT-5.6, Claude Opus 4.7, and Gemini 3.5 across various testing harnesses.
Related event: WolfBench Redefines Agent Evaluation with Five Dimensions(2 posts)→
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