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What is the METR time horizon?
Last reviewed July 16, 2026
The METR time horizon is a capability measure expressed in time: the length of task, measured by how long it takes skilled humans, that an AI agent completes autonomously at a 50% success rate. A model with a 2-hour horizon finishes about half of the tasks that take a person two hours. Tracked across model releases, the horizon has grown exponentially, doubling in months rather than years, which makes it the standard lens on agentic progress.
How the horizon is measured
METR times skilled humans on a portfolio of real tasks (software, research, operations) to calibrate each task's length, then measures which tasks agents complete reliably. The 50% horizon is the task length at which success crosses half. It converts scattered benchmark scores into one interpretable unit: how much unsupervised work you can hand over.
Using the horizon practically
Match task size to horizon: a model with an hours-long horizon handles a bounded refactor or a research brief without checkpoints, not a week of work. It also argues for decomposition: splitting a big job into sub-hour pieces keeps each piece inside the reliable zone, which is exactly what subagent fan-out and task lists do.
Top models on METR time horizon
Full leaderboardPeak scores from the compiled benchmark data; reasoning models use their highest effort tier.
idapt's agentic rankings include a METR horizon lane (shown in minutes and hours), and the platform is built for horizon-sized chunks: subagents fan work out, budgets bound each run, and traces show where a long run drifted.
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