Internal Research Debugging Evaluation
Agents debug difficult problems from OpenAI research work that take researchers hours or days. Scored as mean rubric score.
In-house OpenAI AI self-improvement evaluation. The tasks are not public.
- Level
- Research frontier Researchers, on open problems or tasks that take days
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
GPT-6 Astra leads with 78%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 GPT-6 Astra 78%
- 2 GPT-6.1 Terra 76.4%
- 3 GPT-6.1 Sol 75.5%
- 4 GPT-6 Sol 64.2%
- 5 GPT-5.5 50.5%
- 6 GPT-6 Luna 46.6%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 78% | Sep 2026 | Model card | |
| 2 | GPT-6.1 Terra | 76.4% | Oct 2026 | Model card | |
| 3 | GPT-6.1 Sol | 75.5% | Sep 2026 | Model card | |
| 4 | GPT-6 Sol | 64.2% | Sep 2026 | Model card | |
| 5 | GPT-5.5 | 50.5% | Apr 2026 | Model card | |
| 6 | GPT-6 Luna | 46.6% | Sep 2026 | Model card |
2 models have held number one since its release in Apr 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
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.
12 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 |
|---|---|---|---|---|
| 26 Oct 2026 | GPT-6.1 Terra system card addendum | 76.4 | Internal Research Debugging Evaluation, mean rubric score (Figure 29, 9.0.3.1) | AI R&D risk |
| 29 Sep 2026 | GPT-6.1 Sol system card addendum | 75.5 | Internal Research Debugging Evaluation, mean rubric score (9.1.3.1, Figure 37) | AI R&D risk |
| 22 Sep 2026 | GPT-6 Sol system card | 64.2 | Internal Research Debugging, mean rubric reward, printed chart label (Figure 84, A.8.1.3.1) | AI R&D risk |
| 22 Sep 2026 | GPT-6 Luna system card | 46.6 | Internal Research Debugging, mean rubric reward, printed chart label (Figure 84, A.8.1.3.1) | AI R&D risk |
| 3 Sep 2026 | GPT-6 Astra system card | 78 | Internal Research Debugging Evaluation, score (10.1.3.1) | AI R&D risk |
| 9 Jul 2026 | GPT-5.6 Terra system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 9 Jul 2026 | GPT-5.6 Sol system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 9 Jul 2026 | GPT-5.6 Luna system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 25 Jun 2026 | GPT-5.6 Terra system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 25 Jun 2026 | GPT-5.6 Sol system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 25 Jun 2026 | GPT-5.6 Luna system card | Internal Research Debugging Evaluation, rubric score by cost and latency, chart without printed numbers (Figures 37, 38, 9.1.3.1) | AI R&D risk | |
| 23 Apr 2026 | GPT-5.5 system card | 50.5 | Internal Research Debugging Eval, mean rubric score, printed chart label (Figure 20, 9.1.3.3) | AI R&D risk |
More in AI research
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- 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%