Benchmark Almanac

RSI Bench

GPT-6 Astra 38.9 leads since Sep 2026 Not in model cards

Agents get a starting environment, a GPU budget and a verifier suite for an AI R&D task such as data curation or post-training. The score is mean reward per task, averaged across tasks.

Announced 2026-08-07. Tasks run on Harbor with Modal GPUs. Four sample tasks are public. The leaderboard lists 8 models, led by GPT-6 Astra.

Made by Scale AI Released Aug 2026 AI research tasks Public tasks plus a private test set Last model added Sep 2026 Scores refreshed 2 Nov 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.
8
Models scored on its own leaderboard

GPT-6 Astra leads with 38.9

Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.

  1. 1 GPT-6 Astra max 38.9
  2. 2 GPT-6.1 Sol max 37.0
  3. 3 Claude Opus 5.5 max 35.2
  4. 4 Claude Sonnet 5.5 max 35.2
  5. 5 Qwen 3.8 Max max 33.4
  6. 6 Kimi K3 max 32.8
  7. 7 GLM-5.3 max 32.3
  8. 8 DeepSeek V4.1 Flash max 32.1
#ModelScoreSettingDateSource
1 GPT-6 Astra 38.9 max, Codex Sep 2026 Official leaderboard
2 GPT-6.1 Sol 37.0 max, Codex Sep 2026 Official leaderboard
3 Claude Opus 5.5 35.2 max, Claude Code Sep 2026 Official leaderboard
4 Claude Sonnet 5.5 35.2 max, Claude Code Sep 2026 Official leaderboard
5 Qwen 3.8 Max 33.4 max, terminus-2 Aug 2026 Official leaderboard
6 Kimi K3 32.8 max, terminus-2 Jul 2026 Official leaderboard
7 GLM-5.3 32.3 max, terminus-2 Aug 2026 Official leaderboard
8 DeepSeek V4.1 Flash 32.1 max, terminus-2 Sep 2026 Official leaderboard

3 models have held number one since its release in Aug 2026

Lab claims count from the day the card came out, independent runs from the model’s release.

Moonshot AI Qwen OpenAI
Own leaderboard 3 leaders

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard
32.0 34.0 36.0 38.0 40.0 32.0 34.0 36.0 38.0 40.0

No lab model card reports it yet

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

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%