Introducing MentalHealthBench

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 ⁠ (opens in a new window) 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.

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

Alongside MentalHealthBench, we conducted a separate analysis to compare expert guidance with what people find helpful in AI support. We wanted to check whether responses that experts rated highly could still feel cold or unhelpful to users. By comparing both users’ and experts’ ratings of model responses and the criteria they wrote, we could quantify where their perspectives agreed, differed, or complemented one another. The benchmark’s final scoring criteria are based on expert consensus; this separate analysis did not change them.

We worked with 44 adults who had used AI for mental health or emotional support, representing 16 countries and 14 languages. Participants rated model responses to synthetic conversations and wrote criteria describing what helpful support should look like. Their review was limited to non-acute conversations to avoid exposing them to potentially distressing high-acuity material.

User perspectives highlighted qualities people value in AI support that were less emphasized in expert guidance, particularly practical next steps and tone. Experts placed greater emphasis on gathering relevant context and carefully interpreting ambiguous situations. This comparison gives us a fuller picture of what people value in support and how those preferences relate to clinical guidance.

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.

Scenarios covered by MentalHealthBench. The mix of scenarios is designed to test model responses and does not represent how often these topics occur in ChatGPT.