computer science

METR

METR is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand METR rather than just read about it. In short: Model Evaluation and Threat Research (METR) (MEE-tər), is a nonprofit research institute, based in Berkeley, California, that evaluates frontier AI models' capabilities to carry out long-horizon, agentic tasks that some researchers argue could pose catastrophic risks to society. METR has worked with leading AI companies to conduct pre-deployment model evaluations and contribute to system cards, including OpenAI's o3…

Key takeaways

METR belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect METR to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of METR from memory before moving on to harder problems.

Reference excerpt

Model Evaluation and Threat Research (METR) (MEE-tər), is a nonprofit research institute, based in Berkeley, California, that evaluates frontier AI models' capabilities to carry out long-horizon, agentic tasks that some researchers argue could pose catastrophic risks to society. METR has worked with leading AI companies to conduct pre-deployment model evaluations and contribute to system cards, including OpenAI's o3, o4-mini, GPT-4o and GPT-4.5, and Anthropic's Claude models. METR's CEO and founder is Beth Barnes, a former alignment researcher at OpenAI who left in 2022 to form ARC Evals, the evaluation division of Paul Christiano's Alignment Research Center. In December 2023, ARC Evals was spun off into an independent 501(c)(3) nonprofit and renamed METR.

Research A substantial amount of METR's research is focused on evaluating the capabilities of AI systems to conduct research and development of AI systems themselves, including RE-Bench, a benchmark designed to test whether AIs can "solve research engineering tasks and accelerate AI R&D".

Doubling time estimates

In March 2025, METR published a paper noting that the length of software engineering tasks that the leading AI model could complete had a doubling time of around 7 months between 2019 and 2024. In January 2026, METR released a new version of their time horizon estimates model (Time Horizon 1.1). According to the updated model, the rate of progress of AI capabilities has increased since 2023, with a post-2023 doubling time estimated at 130.8 days (4.3 months). Progress is thus estimated to be 20% more rapid.

Time horizon measurements METR releases a "task-completion time horizon" for analysed AI models. This measures the "task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability." The metric is reported in two variants: the 50%-time horizon, which gives the task duration at which an AI model is estimated to succeed 50% of the time, and the 80%-time horizon, which gives the task duration at which an AI model is estimated to succeed 80% of the time. METR has published two versions of the underlying model: Time Horizon 1.0 and Time Horizon 1.1, the latter introduced in January 2026. As of 9 May 2026, the best-performing model is Claude Mythos, with a 50%-time horizon of likely at least 16 hours and an 80%-time horizon of 3 hours and 6 minutes. METR notes that "[m]easurements above 16 [hours] are unreliable with [their] current task suite". The following table provides time horizon estimates ordered by each model's release date:

References

Notes

Citations

External links Official website

Worked examples

Example 1 — a first encounter with METR

Start with the simplest possible case. Write down what METR claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to METR before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about METR ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of METR

In research: METR appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses METR in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: METR is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2022 establishments in California, 501(c)(3) organizations, AI safety, so understanding it makes those chapters shorter.
In everyday life: Look for METR outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study METR in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what METR means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain METR out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is METR in simple terms?

Why does METR matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study METR?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on METR.