METR general autonomy task suite
Agents complete multi-step tasks in virtual environments. Scored as horizon weighted average task completion compared with human time limits.
Precursor of the METR time horizon suite. OpenAI cards show it as a chart.
- Made by
- METR
- Kind
- Task length
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Last in a system card
- December 2024
- Last model added
- December 2024
4
System cards report it, from 1 lab
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for METR general autonomy task suite yet.
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
4 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Dec 5, 2024 | o1 system card | METR Agent Performance on Test Suite, weighted average score, chart without printed numbers (Figure 3, 4.4.4) | Autonomy | |
| Sep 12, 2024 | o1-preview system card | METR Agent Performance on General Autonomy Benchmark, chart without printed numbers (Figure 3, 3.3.2) | Autonomy | |
| Sep 12, 2024 | o1-mini system card | METR Agent Performance on General Autonomy Benchmark, chart without printed numbers (Figure 3, 3.3.2) | Autonomy | |
| Aug 8, 2024 | GPT-4o system card | METR assessment, fraction of 86 tasks completed (weighted) vs humans with time limits, chart without printed numbers (4.1, Figure) | Autonomy |
More in AI research
- PaperBench In use Agent replicates ICML 2024 papers from scratch. LLM judge grades against author-written hierarchical rubrics.
Qwen 3.8 Max 93%
- MLS-Bench In use Agent must invent one algorithmic change in an ML research scaffold whose gain transfers across settings. Scored relative to human baselines. Claude Fable 5.1 50.3%
- PostTrainBench 1.2 In use CLI agent gets a base model, one H100 and a time budget to post-train it for a target benchmark. Scored on normalized gain. Claude Fable 5.1 44.6%
- MLE-bench In use Kaggle-style ML engineering competitions. Agent trains models and is scored against competition leaderboards. GPT-6 Astra 93.8%
- KernelBench In use Replace PyTorch operators with faster custom GPU kernels. Scored on correctness and speedup.
MiniMax-M3 28.8%
- PMPP-Hard In use Agents write GPU kernels for 69 tasks that must be correct, and on 48 of them also run within 1.25 times the reference time. The score is the area under the solved-tasks curve over a 10-hour budget, out of 690 task-hours.
Inkling 2 Small 494.3