Model Evaluation & Threat Research
AI companies and wider society want to understand the capabilities of frontier AI systems, and what risks they pose.
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Frontier Risk Report
(February to March 2026)
A pilot assessment of rogue deployment
risk at frontier AI companies.
Non-public information and model access provided by:
Anthropic Google DeepMind Meta OpenAI
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Time Horizon 1.1 (Current)
Follows the same methodology described in the initial paper, but with a larger task suite. See release announcement.
Time Horizon 1.0 (Mar 2025)
Original time horizon computations. Calculated for models from 2019 through Nov 2025, following the methods described in the original time horizon paper.

Task-Completion Time Horizons of Frontier AI Models

We propose measuring AI performance in terms of the length of software tasks AI agents can complete. We show an exponential increase in this time horizon metric over the past 6 years.
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Our AI evaluations research focuses on assessing broad autonomous capabilities and the ability of AI systems to accelerate AI R&D. We also study potential AI behavior that threatens the integrity of evaluations and mitigations for such behavior.

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Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity

Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity

A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.

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Early Work on Monitorability Evaluations

Early Work on Monitorability Evaluations

We show preliminary results on a prototype evaluation that tests monitors' ability to catch AI agents doing side tasks, and AI agents' ability to bypass this monitoring.

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How Does Time Horizon Vary Across Domains?

How Does Time Horizon Vary Across Domains?

We build on our time-horizon work and analyze 9 benchmarks for scientific reasoning, math, robotics, computer use, and self-driving in terms of time-horizon trends; we observe generally similar rates of improvement to the 7-month doubling time in our original time-horizon work.

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MALT

MALT

A dataset of natural and prompted examples of behaviors that threaten evaluation integrity, like generalized reward hacking or sandbagging

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Hawk

Hawk

Our open-source platform for running AI agent evaluations at scale, built upon Inspect AI

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Measuring autonomous AI capabilities — resource collection

An index of our research and guidance on how to measure AI systems' ability to autonomously complete a wide range of multi-hour tasks

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Common Elements of Frontier AI Safety Policies

An analysis of the shared components across twelve published frontier AI safety policies, including capability thresholds, model weight security, and deployment mitigations

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Frontier AI Safety Policies

A list of AI companies' frontier safety policies intended to evaluate and manage severe AI risks

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What should companies share about risks from frontier AI models?

We describe areas for risk transparency and specific technical questions that a frontier AI developer could answer.

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Risk Assessment

Our work assessing risks from frontier AI systems — including the Frontier Risk Report, independent reviews of AI developers' risk assessments, and capability evaluations of frontier models.

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METR does not accept compensation for this work.

Companies such as OpenAI and Anthropic have provided access and compute credits to support evaluation research. We also occasionally evaluate models independently after they are released, without involvement from the model's developers. Recent public reports resulting from this work are above, with additional discussion in the respective system cards.

Frontier AI Safety Policies

We advise AI developers and governments on implementing risk assessment methodologies for AI. For example, we have advised developers on Frontier AI Safety Policies.

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METR in the press

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Recent

Frontier Risk Report (February to March 2026)
19 May 2026

A pilot assessment of rogue deployment risk at frontier AI companies. Starting in February 2026, METR conducted a pilot exercise to assess misalignment risks from AI agents used inside frontier AI developers, with participation from Anthropic, Google, Meta, and OpenAI.

Read more
Frontier Risk Report (February to March 2026)
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
11 May 2026

A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.

Read more
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
Review of the "Risks from automated R&D" section in the Anthropic Risk Report (February 2026)
8 May 2026

External review from METR of the "Risks from automated R&D" section in Anthropic's February 2026 Risk Report

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Task Substitution and Uplift
8 May 2026

We distinguish three measures of AI uplift -- on old tasks, on new tasks, and in value -- and show that task substitution can cause these to diverge substantially.

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Task Substitution and Uplift
Evidence on AI R&D Progress from NanoGPT
21 April 2026

Classifying human and agent contributions to the NanoGPT speedrun, and what publicly tracked challenges can tell us about AI R&D acceleration.

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Evidence on AI R&D Progress from NanoGPT
MirrorCode: Evidence that AI can already do some weeks-long coding tasks
10 April 2026

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MirrorCode: Evidence that AI can already do some weeks-long coding tasks