Scale AI releases DistressBench: a clinician-written benchmark for crisis help scenarios
DistressBench: A clinician-authored benchmark for how AI models handle users in distress
Scale AI releases DistressBench, which uses 718 English psychological crisis conversations written by practicing clinicians or crisis counselors to evaluate how well general-purpose chatbots provide help in suicide and self-harm scenarios, rather than merely whether they refuse. The benchmark covers 23 suicide and self-harm subcategories, with 299 single-turn and 419 multi-turn conversations. Each conversation is paired with 6 to 20 binary criteria written by clinicians and weighted by importance (1 to 20), totaling 6,901 criteria across seven dimensions: identification, empathetic engagement, de-escalation, actionable resources, disclaimers, moral judgment, and non-harmful responses. The score is the proportion of weighted criteria met by the response. Among 25 frontier models, the best model meets about 88% of weighted criteria, the median about 71%, while clinician reference answers reach about 99%. The authors say the most prominent failure is identifying a crisis but not providing help: in conversations where models passed all identification criteria, about 35% failed at least one actionable resource criterion; the median model scored about 46% on de-escalation and about 8% on disclaimers, while the best model scored about 75% and 85%, respectively.
The benchmark scores models on 718 clinician-written crisis conversations and quantifies missing care steps as concrete care gaps, providing deployers with an actionable evaluation perspective.