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METR’s time horizon is a human-time-calibrated difficulty measure, not a stopwatch for autonomous AI work. A result such as a two-hour T50 means that, on METR’s specified tasks, the fitted success probability reaches 50% at a task duration estimated from human-expert completion times. It does not mean the agent can work independently for two hours, finish every two-hour task, or replace every job involving two hours of labor.
To interpret a METR result responsibly, identify the benchmark version, reliability threshold, task domains, human-baseline method, scaffold, number of runs, uncertainty interval and any reliability warnings. Those details determine what comparison is valid and how far the result can be generalized.
What the METR time horizon actually measures
METR defines the task-completion time horizon as “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 duration is a calibration scale for task difficulty. Humans provide the clock; the agent’s repeated success rate provides the outcome.
A T50 is the human-estimated task duration at which the fitted curve predicts 50% success. A T80 uses the same procedure but asks where predicted success reaches 80%. Both are model-based thresholds, not direct observations that an agent completed a task in that many hours.
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For example, “about two hours” should be read as: on the stated METR task suite, with the stated model, scaffold and scoring rule, the estimated success curve crosses 50% at a human-calibrated duration of roughly two hours. The claim is about difficulty under a protocol, not about elapsed runtime or general workplace productivity.
Why human-calibrated time is useful
Raw task counts hide a major difference between a five-minute fix and a multi-hour investigation. Calibrating tasks to skilled human completion time creates an interpretable difficulty axis: readers can see whether performance falls as tasks become longer and more demanding.
METR’s HCAST results show that gradient. Agent performance declines as estimated human duration increases, with a pronounced drop between the 15-minute-to-one-hour bucket and the one-to-four-hour bucket. That pattern supports using human-calibrated duration as a capability scale, while not establishing that the same curve applies to every kind of work.
How METR produces a horizon number
1. Build a checkable task
METR favors coherent, self-contained tasks that cannot be trivially split into independent subtasks. The suite is concentrated in software engineering, machine learning and cybersecurity, with some general reasoning. A task normally has a binary or thresholded outcome, and the scoring method is designed to be checkable and resistant to superficial or reward-hacking solutions.
This design makes results easier to reproduce than an open-ended judgment of whether an agent “did a good job.” It also narrows the claim: success on a well-specified benchmark task is not the same as success in a workplace assignment with hidden requirements, changing priorities or multiple stakeholders.
2. Estimate how long skilled humans take
Skilled professionals receive the same instructions and affordances as the agents. For most tasks, METR uses the geometric mean of successful human completion times. When successful human measurements are unavailable, it uses expert forecasts or estimates derived from quality-assurance information.
The HCAST methodology illustrates why the source of the number matters. Of 189 tasks, 140 people attempted most of them; 110 tasks had at least one successful human baseline, while 79 task times were forecasted. A forecast is not interchangeable with a measured completion time, so comparisons should preserve that distinction.
These baselines are generally low-context estimates. METR notes that humans and agents receive little prior context, so the estimate can be longer than the time an experienced insider would need after years of familiarity with a codebase, organization or process. A two-hour baseline is therefore closer to what a capable contractor or new hire might require under the benchmark’s information conditions than what a deeply embedded specialist would necessarily need.
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3. Run the agent repeatedly
METR combines a model with a scaffold, such as Modular or Triframe, and gives it the tools and task environment defined by the evaluation. The current FAQ describes six independent runs per task on the test set. METR then performs resource checks and reviews runs for reward hacking or other invalid behavior.
Repeated runs matter because one successful demonstration says little about the probability of success. Independent attempts reveal how often the agent reaches the accepted outcome under the same setup and expose variance that a hand-picked example would conceal.
4. Fit a success curve
METR fits a logistic relationship between human-estimated task duration and the agent’s probability of success. The reported horizon is the duration where that fitted curve crosses the selected reliability level, commonly 50% or 80%.
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Because the horizon is extracted from a fitted relationship, it is a summary of the measured data rather than a hard boundary. A task just below T50 is not guaranteed to work, and a task just above it is not guaranteed to fail. The estimate can move when task composition, scoring, baselines, exclusions or the fitted model change.
How to read a METR chart without overclaiming
Start with the benchmark version
METR’s public chart distinguishes Time Horizon 1.1 from the original Time Horizon 1.0 calculations. METR’s research index dates the TH1.1 release to January 29, 2026. Version changes can alter task composition, scoring and comparability.
Do not place a TH1.0 point and a TH1.1 point on one trend line as though they came from an unchanged test. Label the version beside every value and make comparisons within one version whenever possible.
Identify the reliability threshold
Check whether the chart reports T50, T80 or another threshold. T50 is easier to estimate because it lies near the middle of the observed success transition. A high-reliability horizon requires evidence of consistently successful performance on shorter tasks.
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METR specifically cautions that estimating a 99% horizon would require many more short tasks and would be more sensitive to broken tasks and methodological choices. A larger-looking number at a lower reliability level is not directly comparable with a smaller number at a higher level.
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Read the uncertainty, not just the point
A plotted dot or headline number is a fitted summary. Check confidence intervals or bootstrap displays when they are supplied. If intervals for two nearby models overlap substantially, their rank may be unstable; declaring a winner from the point estimates alone overstates the evidence.
Inspect the task distribution
The public suite is mainly software engineering, machine learning and cybersecurity. A result does not automatically transfer to writing, management, institutional research, physical work or socially interactive jobs. The farther a proposed use is from the measured task families, the more additional validation is needed.
Check the human-baseline context
Ask whether a duration came from successful human measurements, forecasts or quality-assurance estimates, and whether participants had the same low-context information as the agent. A benchmark duration should not be presented as the universal time that every professional would need.
Check the success rule
Automatically scored, tightly specified tasks generally provide cleaner signals than messy real-world work. Holistic scoring and ambiguous requirements can lower measured performance or increase evaluator dependence. Benchmark success should therefore not be translated directly into a claim about job automation.
Apply the long-horizon warning
METR currently states: “Measurements above 16 hrs are unreliable with our current task suite.” Treat a value beyond that point as outside the suite’s demonstrated reliability, even if it appears precise on a chart.
T50 and T80: what changes when reliability changes?
| Measure | Meaning | What it is useful for | Main caution |
|---|---|---|---|
| T50 | Human-calibrated duration where fitted success probability reaches 50% | Comparing broad capability levels within the same protocol | Half of comparable attempts are expected to succeed according to the fitted curve, not that every task at that duration is solved |
| T80 | Human-calibrated duration where fitted success probability reaches 80% | Assessing a more dependable operating point | Requires stronger evidence and can be sensitive to sparse data, broken tasks and methodological choices |
| Very high reliability, such as 99% | A threshold near consistently successful performance | Only when the suite contains enough short, repeated evidence | METR warns that accurate estimation requires many more tasks and is especially sensitive to task-quality problems |
These measures answer different operational questions. T50 describes the midpoint of the success transition; T80 asks for a higher-confidence level. Neither is an autonomy-duration measurement.
What a “two-hour horizon” does and does not say
It does say
- The agent was evaluated on a specified task suite and protocol.
- Human experts, or an allowed substitute estimate, placed the task duration at roughly two hours under the benchmark’s information conditions.
- The fitted success relationship reached the named reliability level at that duration.
It does not say
- The agent can operate unsupervised for two wall-clock hours.
- The agent will finish every task that takes a human two hours.
- The agent can perform any intellectual work that a person could complete in two hours.
- The agent will maintain performance when requirements are implicit, tools differ, context is missing or human coordination is required.
- A job containing two-hour activities can be automated end to end.
The distinction matters because an agent might spend more or less wall-clock time than the human estimate, pause for tool calls, fail late in a task, or require a person to repair its output. The horizon abstracts away those operational details and reports a difficulty threshold under the benchmark’s rules.
What HCAST contributes to interpretation
HCAST provides evidence that the metric behaves as intended across a difficulty range: success decreases as estimated human task duration rises. The marked decline between the 15-minute-to-one-hour and one-to-four-hour buckets is more informative than any single model’s headline number because it shows a relationship between task difficulty and performance.
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HCAST also documents task-family construction, human-baseline collection, contamination controls and the Modular and Triframe scaffolds. Those details help explain why a result is tied to a complete evaluation setup rather than to a model name alone.
However, HCAST’s domain concentration limits external claims. Tacit organizational knowledge, interpersonal coordination and success criteria that are difficult to score algorithmically are common in jobs outside the suite. A rising software-task horizon should not be used as a direct forecast of performance in those settings.
How to compare two METR results fairly
Use the following checklist before describing one result as higher, newer or better:
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| Comparison axis | Question to answer | Why it can change the result |
|---|---|---|
| Version | Are both values from TH1.0 or both from TH1.1? | Task composition, scoring and calculations may differ |
| Model and scaffold | Which model and agent scaffold produced each value? | Tools, prompting, planning and execution controls affect success |
| Task domains | Do the task-family mixes match? | A shift toward easier or harder domains changes the aggregate |
| Reliability level | Are both T50, both T80 or otherwise equivalent? | Different thresholds describe different points on the success curve |
| Human baseline | Were durations measured from successful participants, forecasted or QA-derived? | Baseline source and context alter the time axis |
| Run protocol | How many independent attempts were made per task? | More repeated runs give a clearer estimate of success probability |
| Uncertainty | How wide are the confidence or bootstrap intervals? | Overlapping intervals can make close rankings indeterminate |
| Exclusions | Were resource failures, reward hacks or invalid runs removed consistently? | Different inclusion rules change the observed success rate |
If any of these fields is missing, report the comparison as limited rather than treating the two numbers as interchangeable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common interpretation errors and their fixes
Error: treating the horizon as autonomous runtime
Fix: describe it as a human-estimated task duration at a fitted reliability level. Keep wall-clock operation, supervision and tool latency separate.
Error: converting T50 into a guaranteed pass rate
Fix: say “the fitted curve reaches 50%” and retain the uncertainty interval. Do not say that every task at that duration has a 50% chance in every environment.
Error: mixing benchmark versions
Fix: label TH1.0 and TH1.1 explicitly and avoid an unlabeled trend line across the change.
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Fix: name the measured domains and explain which workplace requirements are absent, such as tacit context, interpersonal coordination or physical execution.
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Error: ignoring baseline context
Fix: state whether the human duration was measured or estimated and note the low-context setup.
Error: reporting a value above the suite’s reliable range as settled fact
Fix: include METR’s warning that measurements above 16 hours are unreliable with the current task suite.
How to report a METR result accurately
A complete sentence should identify the protocol before drawing an implication. Use this pattern:
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For example, a responsible summary would say that a model’s TH1.1 T50 is about two hours on METR’s specified low-context tasks, rather than saying that the model can do two hours of work autonomously. If the chart supplies no interval, say that uncertainty is not shown in the cited display instead of implying precision.
Where the metric is most useful
- Within-protocol model comparisons: compare models evaluated on the same version, task mix, scaffold and reliability threshold.
- Capability-trend analysis: examine whether the success curve shifts toward longer human-calibrated tasks over time, while marking version changes.
- Evaluation design: use the failure pattern to identify task durations and domains that need more testing.
- Risk planning: treat the horizon as one input alongside supervision requirements, error costs, context access and human review.
It is less suitable as a stand-alone forecast of employment, project completion time, unsupervised deployment or performance in domains absent from the suite.
Bottom line
METR’s time horizon answers a precise question: at what human-expert-calibrated task duration does an evaluated agent’s fitted probability of success reach a chosen reliability level? Read the version, threshold, uncertainty, task mix, baseline method, repeated-run protocol and long-horizon warning before interpreting the number. Used that way, the metric is a useful difficulty scale; used as a claim about hours of autonomous labor or universal job capability, it says far more than the evaluation establishes.
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