Taste-Bench: AI agents make poor long-term strategic choices

Taste-Bench tests how well agents pick strategic paths in long-running tasks. The best model, GPT-5.6 Sol, chooses correctly on just under 60 percent of simple binary questions, and thinking longer does not help.

Why it matters: Agents are increasingly trusted with multi-step work, and this points to judgment, not raw capability, as a bottleneck. That extra test-time reasoning does not help suggests the gap needs different training rather than more compute.