- METR defines the 50% time horizon as the task duration, measured by human expert completion time, at which an AI agent is predicted to succeed half the time.
- METR says the horizon measures the difficulty of a task, not the time an AI spends on it, and 80% horizons are 4 to 6 times shorter than 50% horizons.
- METR's time-horizons page says measurements above 16 hours are unreliable with its current task suite.
METR, the group that publishes the time-horizon measure, says measurements above 16 hours are unreliable with its current task suite. That caveat sits on the page that reports how long a task AI agents can handle.
The task-completion time horizon is the task duration, measured by how long human experts take, at which an AI agent is predicted to succeed with a given level of reliability. The 50% time horizon is the duration at which the agent is predicted to succeed half the time. METR's page stresses that it measures the difficulty of a task, not how long the AI spends on it.
The human side comes first. For most tasks, METR contracts people to attempt the task and uses the geometric mean of their successful completion times as its length; for some tasks it relies instead on expert estimates or completion times from QA tests. In the 2025 paper that introduced the measure, METR collected over 800 human baselines totalling 2,529 hours, from skilled professionals in software engineering, machine learning and cybersecurity with about five years of relevant experience.
The paper's 170 tasks came from three sets: HCAST, with 97 tasks from about one minute to 30 hours; RE-Bench, with 7 machine-learning research tasks of about 8 hours each; and SWAA, with 66 single-step tasks of 1 to 30 seconds. METR's current page describes a suite of over a hundred software, machine-learning and cybersecurity tasks, drawing on RE-Bench, HCAST and a set of shorter novel software tasks.
The AI side is a curve. For each model, METR fits a logistic curve that predicts the probability of success against task length, with length on a log scale. The 50% horizon is where the curve crosses one half. METR runs each task six times, which gives roughly 1,000 runs for the test set.
The paper's headline example is o3, with a 50% time horizon of around 110 minutes. The same method at an 80% success threshold gives horizons 4 to 6 times shorter, so a horizon is only meaningful with its reliability level beside it.
The paper also measured a trend. The paper says the frontier AI time horizon has doubled approximately every seven months since 2019; its regression gives 207 days, with a 95% bootstrapped confidence interval of 166 to 240 days. METR's page says that for models released between early 2019 and March 2025, an exponential trend is a much better fit to the data than both the linear and hyperbolic trends.
The limits are stated by METR. The paper says a time horizon is always measured relative to a domain and a task distribution, and that realistic tasks rarely have automatic scoring. It also says that, because the dataset is limited, it cannot confidently measure time horizons at very high success rates, such as 95%. The page adds the 16-hour caveat, says it is no longer actively updated, and was last updated on 8 September 2026.
What it means for you Our view
A time horizon describes how hard a task is for people, so it does not tell you how long an agent will take to run it. A figure only means something with its success level beside it, because the gap between the 50% and 80% horizons is large. METR also says its page is no longer actively updated, so the newest models may not be on it. When you see a claim that an agent can do tasks of a given length, check which success level it uses, and whether the length is inside the range METR says it can measure.
TAGSBenchmarksMETRtime horizons
