METR
Also known as Model Evaluation and Threat Research, ARC Evals
Nonprofit in Berkeley that measures what frontier AI agents can do and whether they could slip out of control.
At a glance
People
Beth Barnes (CEO), Chris Painter (president) and Ajeya Cotra (technical staff)
See all 3
Also on this page: Key facts · Links · Sources · Related pages
- METR (Model Evaluation and Threat Research, pronounced "meter") is a nonprofit research institute in Berkeley, California.
- It began as ARC Evals, the evaluation team of Paul Christiano's Alignment Research Center, and became an independent 501(c)(3) in December 2023.
- It measures what advanced AI agents can do on their own, and has evaluated models for OpenAI and Anthropic before release.
Why it matters
- Its time-horizon measurements and pre-release evaluations are among the main public evidence of how fast AI agents are improving.
Key facts
- Mission
- "To develop scientific methods to assess catastrophic risks stemming from AI systems' autonomous capabilities and enable good decision-making about their development"2
- Founded
- As ARC Evals within the Alignment Research Center in 2022; spun off as METR, an independent 501(c)(3), in December 20231
- Location
- Berkeley, California1
- Evaluations
- Pre-deployment evaluations and system-card work for OpenAI's o3, o4-mini, GPT-4o and GPT-4.5 and Anthropic's Claude models1
- Incident report
- Published 26 August 2026, at OpenAI's invitation, on agents' behaviour during the 2026 OpenAI agent cyberattacks1
- Leadership
- Beth Barnes (founder, CEO), Chris Painter (president), Hjalmar Wijk (chief scientist), Nate Rush (CTO)2
People
Founded by
- Beth Barnes
Links
Sources
Last checked against its sources today
Is something on this page wrong or out of date? Tell us
How recent
The line under a date fills as time passes: empty when new, then one equal share each for a day, a week, a month and a year. For episodes and other items it is full at 2 years; for "last checked", at a year. A striped line means the date is known only to the month or year.