Benchmark Almanac

DeepRSI

Claude Opus 5.5 1,629 leads since Oct 2026 Not in model cards

15 open-ended ML tasks with a 48-hour budget each. Private verifiers score gains over baselines. Models ranked by Bradley-Terry Elo.

Tasks come from proprietary research. Repo has only 5 sample tasks. Agents see a public verifier, but only the private one counts.

Made by Hillclimb Released Oct 2026 AI research tasks Tasks kept private 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.
hclimb/deeprsi-samples 1 stars Leaderboard
7
Models scored on its own leaderboard

Claude Opus 5.5 leads with 1,629

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.

Bars start at 600.

  1. 1,629 Claude Opus 5.5
  2. 1,548 Claude Fable 5.1
  3. 1,232 GPT-6 Astra
  4. 965 Gemini 4 Argon
  5. 820 Kimi K3
  6. 685 Grok 4.7
  7. 643 Gemini 3.8 Flash

Bars start at 600.

  1. 1 Claude Opus 5.5 max 1,629
  2. 2 Claude Fable 5.1 max 1,548
  3. 3 GPT-6 Astra max 1,232
  4. 4 Gemini 4 Argon high 965
  5. 5 Kimi K3 max 820
  6. 6 Grok 4.7 xhigh 685
  7. 7 Gemini 3.8 Flash high 643
#ModelScoreSettingDateSource
1 Claude Opus 5.5 1,629 max Sep 2026 Official leaderboard
2 Claude Fable 5.1 1,548 max Sep 2026 Official leaderboard
3 GPT-6 Astra 1,232 max Sep 2026 Official leaderboard
4 Gemini 4 Argon 965 high Sep 2026 Official leaderboard
5 Kimi K3 820 max Jul 2026 Official leaderboard
6 Grok 4.7 685 xhigh Sep 2026 Official leaderboard
7 Gemini 3.8 Flash 643 high Sep 2026 Official leaderboard

Number one over time

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

Anthropic
Own leaderboard 1 leader
Claude Opus 5.5 1,629

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
750 1,000 1,250 1,500 1,750 750 1,000 1,250 1,500 1,750

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%