Since early 2025, we've been studying how AI tools impact productivity among developers. Previously, we found a 20% slowdown. That finding is now outdated. Speedups now seem likely, but changes in developer behavior make our new results unreliable. We’re working to address this.

We estimate that GPT-5.3-Codex with reasoning effort `high` (not `xhigh`) has a 50%-time-horizon of around 6.5 hours (95% CI of 3 hrs to 17 hrs) on our suite of software tasks. OpenAI provided API access for this evaluation.

We estimate that Claude Opus 4.6 has a 50%-time-horizon of around 14.5 hours (95% CI of 6 hrs to 98 hrs) on software tasks. While this is the highest point estimate we’ve reported, this measurement is extremely noisy because our current task suite is nearly saturated.

We estimate that GPT-5.2 with `high` (not `xhigh`) reasoning effort has a 50%-time-horizon of around 6.6 hrs (95% CI of 3 hr 20 min to 17 hr 30 min) on our expanded suite of software tasks. This is the highest estimate for a time horizon measurement we have reported to date.

We’ve started to measure time horizons for recent models using our updated methodology. On this expanded suite of software tasks, we estimate that Gemini 3 Pro has a 50%-time-horizon of around 4 hrs (95% CI of 2 hr 10 mins to 7 hrs 20 mins).

We’re updating the way we measure model time horizons on software tasks (TH 1.0→1.1). The updated methodology incorporates more of the tasks from HCAST, expanding our total from 170 to 228. This produces tighter estimates, especially at longer horizons.

Today we published a critique of @metr.org’s time-horizon methodology by one of the paper’s lead authors, Thomas Kwa Link: metr.org/notes/2026-0...

How well can AI-based monitoring detect when an agent is covertly pursuing a side objective? In early work, we find clear trends: more capable models (in terms of time horizon) are better able to detect covert behavior.

We estimate that Claude Opus 4.1 has a 50%-time-horizon of around 1 hr 45 min (95% confidence interval of 50 to 195 minutes) on our agentic multi-step software engineering tasks. This estimate is lower than the current highest time-horizon point estimate of around 2 hr 15 min.

We tested how autonomous AI agents perform on real software tasks from our recent developer productivity RCT. We found a gap between algorithmic scoring and real-world usability that may help explain why AI benchmarks feel disconnected from reality.

Before publishing our recent developer productivity RCT, we thought hard about how to accurately and clearly communicate our results. In a new blog post, we outline some of our key considerations regarding scientific integrity and communication.

Prior work has found that Chain of Thought (CoT) can be unfaithful. Should we then ignore what it says? In new research, we find that the CoT is informative about LLM cognition as long as the cognition is complex enough that it can’t be performed in a single forward pass.

In a new report, we evaluate whether GPT-5 poses significant catastrophic risks via AI R&D acceleration, rogue replication, or sabotage of AI labs. We conclude that this seems unlikely. However, capability trends continue rapidly, and models display increasing eval awareness.

We found that Grok 4’s 50%-time-horizon on our agentic multi-step software engineering tasks is about 1hr 50min (with a 95% CI of 48min to 3hr 52min) compared to o3 (previous SOTA) at about 1hr 30min. However, Grok 4’s time horizon is below SOTA at higher success rate thresholds.

We have open-sourced anonymized data and core analysis code for our developer productivity RCT. The paper is also live on arXiv, with two new sections: One discussing alternative uncertainty estimation methods, and a new 'bias from developer recruitment' factor that has unclear effect on slowdown.

METR previously estimated that the time horizon of AI agents on software tasks is doubling every 7 months. We have now analyzed 9 other benchmarks for scientific reasoning, math, robotics, computer use, and self-driving; we observe generally similar rates of improvement.

METR a few months ago had two projects going in parallel: a project experimenting with AI researcher interviews to track degree of AI R&D acceleration/delegation, and this project. When the results started coming back from this project, we put the survey-only project on ice.

We ran a randomized controlled trial to see how much AI coding tools speed up experienced open-source developers. The results surprised us: Developers thought they were 20% faster with AI tools, but they were actually 19% slower when they had access to AI than when they didn't.

In measurements using our set of multi-step software and reasoning tasks, Claude 4 Opus and Sonnet reach 50%-time-horizon point estimates of about 80 and 65 minutes, respectively.

At METR, we’ve seen increasingly sophisticated examples of “reward hacking” on our tasks: models trying to subvert or exploit the environment or scoring code to obtain a higher score. In a new post, we discuss this phenomenon and share some especially crafty instances we’ve seen.

When will AI systems be able to carry out long projects independently? In new research, we find a kind of “Moore’s Law for AI agents”: the length of tasks that AIs can do is doubling about every 7 months.

How close are current AI agents to automating AI research itself? Our new ML research engineering benchmark (RE-Bench) addresses this question by directly comparing frontier models such as Claude 3.5 Sonnet and o1-preview with 50+ human experts on 7 challenging research engineering tasks.

Testing, testing