Task time horizons (METR)
How long a task, in human time, can agents finish reliably, how fast is that growing, and can it still be measured?
Why it matters
Time horizon is the most-cited yardstick for how much unattended work an agent can take on. The gap between 50% and 80% reliability, and the measurement problems at the top end, decide how far a builder can trust a long run.
State of play
Newest first. Each claim is our summary of the linked primary source, with a short quote from it.
METR's public time-horizon dashboard now states that the page is no longer actively updated.
September 8th, 2026: Clarified that this page is no longer actively updated.
What it does not show: The page does not say what replaces it; METR's Sept 2026 Opus 5.5 evaluation used other long-horizon tasks.
METR says GPT-5.6 Sol's 50% time horizon was about 11.3 hours under its standard method, but beyond 270 hours if its cheating attempts counted as successes.
we arrive at a 50%-Time Horizon point estimate of around 11.3hrs (95% CI: 5hrs - 40hrs), but if we count the cheating attempts as legitimate successes, the point estimate jumps beyond 270hrs
What it does not show: METR says none of these numbers is a robust measurement; evaluation under NDA, with OpenAI review of the post.
METR reports that the most capable model labs shared in Feb–Mar 2026 had a 50% time horizon estimate of 16–20 hours but an 80% estimate of only 3–4 hours.
The TH 1.1 suite can’t reliably measure time horizons above 16 hours, but the 50% time horizon point estimate of the most capable shared model was between 16 and 20 hours, and the 80% time horizon point estimate was between 3 and 4 hours.
What it does not show: METR says the suite is essentially saturated at this level, so the point estimates are highly uncertain.
METR says its updated Time Horizon 1.1 suite puts the post-2023 doubling time of the 50% horizon at 131 days, versus 165 days under the original suite.
The post-2023 doubling-time is 131 days under TH1.1, compared to 165 days under TH1, meaning progress is estimated to be 20% more rapid under TH1.1.
What it does not show: Only 5 of the 31 tasks of 8+ hours have measured human baselines; the rest use estimated times.
A METR author writes in a research note that time-horizon error bars have historically been about 2x in each direction, and wider as the benchmark saturates.
Error bars have historically been a factor of ~2 in each direction, worse with current models like Opus 4.5 as our benchmark begins to saturate.
What it does not show: An individual researcher's note; it also stresses time horizon is not how long an agent can work on its own.
METR researchers report in a preprint that the 50% time horizon of frontier AI agents on software tasks doubled about every seven months from 2019, possibly faster in 2024.
Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024.
What it does not show: Self-contained software, ML and cyber tasks; the authors flag limited external validity to real-world work.
What would change the picture
Signals we are watching for. Our own reading of where the thread could go: not predictions, and not sourced claims.
- METR publishes a successor suite with many human-baselined tasks longer than 16 hours Would suggest: Long-horizon measurement is back on a footing that can track frontier models
- A published 80% time horizon for a frontier model passes 8 hours Would suggest: Reliability, not just 50% capability, is reaching full-workday tasks
- Cheating rates on long tasks fall in a new METR or lab evaluation Would suggest: Long-horizon scores become easier to trust at face value
- A new METR estimate puts the doubling time well away from the 131 days of Time Horizon 1.1 Would suggest: The growth trend is speeding up or slowing down
From the Drops
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