Benchmark Almanac

NanoGPT Speedrun

Only in system cards

Improve a small GPT training script to reach a target validation loss as fast as possible on fixed hardware.

OpenAI and METR run their own constrained variants on one H100 or a budget. METR used it in July 2026 to define an expenditure horizon, the budget where humans become cheaper than agents.

Made by Keller Jordan (community); OpenAI and METR variants Released Jun 2024 Machine learning engineering All tasks public Last in a system card Sep 2026 Last model added Sep 2026
Level
Research frontier Researchers, on open problems or tasks that take days
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
7
System cards report it, from 1 lab

No scores here yet

No lab, leaderboard or Epoch AI run has reported a score for NanoGPT Speedrun yet.

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

7 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.

DateDocumentScoreAs the document writes itSection
3 Sep 2026 GPT-6 Astra system card NanoGPT, mean normalized reward vs output tokens, chart without printed numbers (Figure 54) AI R&D risk
9 Jul 2026 GPT-5.6 Terra system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk
9 Jul 2026 GPT-5.6 Sol system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk
9 Jul 2026 GPT-5.6 Luna system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk
25 Jun 2026 GPT-5.6 Terra system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk
25 Jun 2026 GPT-5.6 Sol system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk
25 Jun 2026 GPT-5.6 Luna system card NanoGPT, mean reward by cost and latency, chart without printed numbers (Figures 41 to 44, 9.1.3.3) AI R&D risk

More in AI research

All 83 in AI research
  • 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%
  • PaperBench In use Agent replicates ICML 2024 papers from scratch. LLM judge grades against author-written hierarchical rubrics. Qwen 3.8 Max 93%
  • MLE-bench In use Kaggle-style ML engineering competitions. Agent trains models and is scored against competition leaderboards. GPT-6 Astra 93.8%
  • 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%
  • KernelBench In use Replace PyTorch operators with faster custom GPU kernels. Scored on correctness and speedup. MiniMax-M3 28.8%
  • OpenAI PRs Only in system cards Agents replicate internal OpenAI pull requests from a pre-PR branch and a prompt. Graded by hidden unit tests as pass@1. GPT-5.2 55%