Planning Benchmark scores agent coverage and plan quality

This is a useful benchmark because it measures an earlier, easier-to-ignore failure mode: agents forgetting requirements before they ever write code. The caveat is that it also measures harness quality, so the score reflects the workflow around the model as much as the model itself.

• –The frozen requirement catalog and full/partial/missing scoring make it harder to hand-wave than a typical demo or screenshot benchmark
• –Because the task is planning-only, it highlights attention and spec retention rather than raw coding ability
• –The video’s comparison across models suggests tool orchestration and planning mode can materially change outcomes, not just model choice
• –For teams adopting coding agents, this is a better proxy for “will it miss features?” than pass/fail coding scores
• –It is still a narrow eval: strong performance here does not guarantee good implementation, debugging, or ambiguity handling