METR: Claude Opus 4.5 has a 50% task completion time horizon of about 4 hours and 49 minutes, more than double that of Claude Opus 4 released earlier this year
We estimate that, on our tasks, Claude Opus 4.5 has a 50%-time horizon of around 4 hrs 49 mins (95% confidence interval of 1 hr 49 mins to 20 hrs 25 mins). While we're still working through evaluations for other recent models, this is our highest published time horizon to date. [image]
@metr_evals
Context & Ripple Effects
METR’s estimate gives a task-duration measure for Claude Opus 4.5, rather than a simple speed or benchmark comparison: at the 50% point, it places the model at roughly 4 hours and 49 minutes, more than twice the earlier Opus 4 result. The broad confidence interval means the result is a directional capability signal, not a precise guarantee for any individual workflow.
The result sits alongside evidence of a product line being optimized across several dimensions. Earlier coverage described Sonnet 4.5 as faster and more steerable in Claude Code, while later reporting on Opus 4.6’s fast mode highlights a quality, latency, and cost trade-off rather than a single performance metric.
First-order effects
Teams evaluating Claude Opus 4.5 can consider delegating longer, multi-step tasks to the model before expecting human intervention, while still designing for failures beyond the reported 50% completion point.
Anthropic gains an independently reported indicator that Opus 4.5 can sustain longer task sequences than Opus 4; the estimate’s wide interval limits how strongly buyers should generalize it to production work.
Second-order effects
Model selection shifts from comparing token price or response speed alone toward comparing the cost and reliability of completing an entire task—a relevant distinction given Opus 4.5’s lower token pricing versus Opus 4.1.
Rival model providers and evaluation groups face pressure to publish comparable long-horizon, agent-style results, while enterprise users will need workload-specific tests to determine whether longer runs reduce or increase oversight costs.
Third-order effects
If time horizons continue to improve, AI product competition will increasingly center on reliable execution of extended workflows, not isolated answers; inference capacity, tool orchestration, and monitoring become more consequential parts of the offering.
The metric also reinforces the need for durable safeguards around models focusing on the hardest parts of a task: longer autonomous runs can make a single successful delegation more valuable, but raise the cost of errors that persist through many steps.
The trend: Frontier AI is moving from short-turn assistance toward longer-running task execution, making effective cost, reliability, and operational control central competitive measures.
Happy to have this out, there was a bunch of work that went into validating this result that I was really impressed by—just careful, meticulous rigor.
@idavidrein
David Rein on x
While I have a lot of uncertainty about this result (look at the confidence intervals!!), I do think this is just a clear example of what being on an exponential trend means, in terms of what it *feels like*. You gotta price this sort of thing in
@nikolaj2030
Nikola Jurkovic on x
This result updates me towards 4 month doubling times being my median estimate for the next two years. That means by EOY 2026 the time horizon will be 40 hours, and by EOY 2027 it will be 320 hours.
@aidigest_
@aidigest_ on x
Opus 4.5 puts the world roughly back on track for the red line 😬 Every ~4 months, the length of coding tasks AI agents can perform (compared to human professionals) *doubles* More context on this finding in @METR_Evals thread https://x.com/... [image]
@ninoyako93
@ninoyako93 on x
The expontential trends for AI capabilities are accelerating, pushing progress into super-exponential trajectories. The next few years will be so transforming, people wont be able recognize the world they live in anymore.
@jamesdmoore614
James Moore on x
https://www.youtube.com/... Mathematical proof behind the “100x improvement” prediction shows that reliability is scaling log-linearly, which is exactly what is needed to turn AI from a “chatbot” into the “autonomous worker” the Moonshots team predicted. @PeterDiamandis
@spoonedher
@spoonedher on x
few things to keep in mind 1. the confidence interval is insane and should raise suspicion 2. this is the 50% time horizon. super exponential relies on the 80% which is paltry 3. their sample size is really small and therefore exploitable through rlvr
@daveshapi
David Shapiro on x
I downloaded the latest data and repeated my previous exercises from August of this year to determine the current “best fit” model. TLDR: we are looking at a log quadratic fit. When we forecast this out, we're looking at “human equivalent task length” quickly stretching into [ima…
@shakeelhashim
Shakeel on x
Yesterday I wrote that the performance of AI models on METRs time horizon tasks had increased by 4x this year. Turns out it's actually 7x. Makes my point from yesterday even more valid, I think — despite all the talk of AI progress slowing down this year, things have continued [i…
@downingark
Frank Downing on x
Expanding time horizons on this eval represent AI's increasing ability to reliably deliver leverage on human labor. As basis point improvements on PHD level quizzes seem less and less representative of real world performance, the METR study is increasingly relevant.
@tyler_m_john
Tyler John on x
The long awaited Opus results. Opus breaks the task suite, which doesn't have long enough tasks for it to do. METR can't confidently rule out a 20 hour time horizon, which would about a year ahead of schedule. Epoch predicts Gemini 3 will be even better.
@liron
Liron Shapira on x
This AGI capability scale didn't even exist before 2020, yet whatever tech cascade it's measuring is incredibly robust, and shows no sign of S curving before blowing past human level. This is the WaitButWhy train whizzing past human station scenario.
@peterwildeford
Peter Wildeford on x
Recent METR data shows ~4.4mo doubling for 50% reliability, with frontier models now completing median tasks up to ~5 hours. Which is crazy! 80% reliability fits similar ~4.5mo, but w/ more variance. Reliability gains may be plateauing somewhat? Worth monitoring. [image]
@deanwball
Dean W. Ball on x
I wonder if the average software engineer, drawn from the worldwide population, achieves a >50% success rate on tasks that take METR's sample engineers ~4.5 hours.
@ryanpgreenblatt
Ryan Greenblatt on x
I've updated towards a higher chance of much faster than trend AI progress in 2026. I also generally updated towards (somewhat) faster doubling times in the next 2 years. I was expecting ~170 day doubling times (5.7 months) and now I expect ~150 day doubling times (5 months).
@giansegato
Gian on x
i don't think people have internalized what 2026-27 are set to look like [image]
@emollick
Ethan Mollick on x
So, Claude 4.5 came in far above trend in the much-watched METR measure of the task duration that AI can accomplish autonomously at 4 hours 49 minutes. Interestingly, at the harder 80% success threshold, it is GPT-5.1 Codex Max that breaks the trend. In 2023, GPT-4 was a minute. …
@dmnd.me
Jeremy Diamond on bluesky
There's a bur after you get past the toy/novelty uses where the learning curve gets steep fast. — I don't see a lot of discussion about how much more effort and scaffolding you need to put in to enable these products to run this long and actually produce good output metr.org/bl…
@hern
Alex Hern on bluesky
METR has updated its LLM time-horizon chart for Claude 4.5 Sonnet and we're still above the trendline. Slowdown? What slowdown? metr.org/blog/2025-03... [image]
@emollick
Ethan Mollick on bluesky
Since people are asking about the “exponential growth” bit - AI has a jagged frontier but on most hard careful measures of AI ability we are seeing rapid exponential gains until benchmarks get saturated. — Take METR long tasks: metr.org/blog/2025-03... [image]
@moskov.goodventures.org
Dustin Moskovitz on bluesky
Cross-posting this one from METR. If you've seen this graph before, you're probably going to be pretty interested in the new update. — If you've never seen this graph before, please just ignore this post. Just a guy and his charts, nothing to see here. [image]
r/singularity
r on reddit
METR finds Opus 4.5 has a 50% time horizon of 4 hours 49 minutes