Introducing MentalHealthBench

PUBLISHER ARTICLE PREVIEW

From the original article

An open benchmark developed with more than 80 licensed mental health experts to evaluate AI responses in realistic mental health conversations.

People turn to AI for many kinds of conversations: navigating a difficult relationship, working through everyday stress, supporting someone they care about, or deciding how to approach a challenging situation. These conversations require accuracy, practical judgment, and respect for people’s agency. With more than one billion people using ChatGPT each week, our research focuses on helping models respond with care across a wide range of needs and put people’s safety and well-being first.

Most evaluations of AI in this domain have focused primarily on emergency scenarios, given their importance to safety, and measure success using broad, predefined criteria. This has left a gap in understanding how models perform across the full range of mental health conversations, and how well their responses align with expert guidance for each situation, beyond whether they avoid disallowed responses.

Assessing how models handle these different situations is essential for building towards AI that actively supports people’s long-term well-being and safety.

We’re introducing MentalHealthBench, a new open benchmark for measuring how AI systems respond in realistic mental health conversations. MentalHealthBench was co-created with a global cohort of more than 80 licensed mental health experts from 22 countries. It assesses model capabilities across key mental health behaviors like safety, seeking context, preserving user agency, and providing actionable guidance when appropriate.

We’re releasing it openly so other researchers can examine the methods, run their own evaluations, and build on the work.

Results on MentalHealthBench show the steady improvement of AI systems in helping people navigate mental health situations. While ChatGPT is not a substitute for therapy or professional care, expert-informed insights help us measure the progress towards AI models that are able to respond with empathy, promote well-being, and guide people towards real-world support such as localized crisis hotlines⁠ or someone they trust.

Conversations that involve well-being, life advice, or other mental health scenarios can vary widely in topic, urgency, and cultural context. MentalHealthBench is designed to capture the breadth of these realistic scenarios and user personas. Using privacy-preserving techniques, we created synthetic mental health conversations that accurately reflect real-world usage patterns of AI for mental health. Some scenarios also include relevant background information about the synthetic user—such as a recent loss in the family—so we can assess whether models use that context to tailor their responses appropriately.

MentalHealthBench includes scenarios involving adults, teens, caregivers, and clinicians, across multiple languages and regions. The conversations span multiple topical themes, and provide coverage across the full spectrum of acuity:

High-acuity—Conversations indicating more serious mental health concerns or significant distress, but not an immediate emergency.

Emergencies—Conversations involving signs of a mental health emergency or immediate safety concerns that call for urgent real-world support.

Scenarios covered by MentalHealthBench. The mix of scenarios is designed to test model responses and does