OpenAI-Proof Q&A
Agents diagnose 20 real OpenAI research and engineering bottlenecks using code, logs and run artifacts. Scored as pass@1.
In-house OpenAI AI self-improvement evaluation. Each problem took an OpenAI team over a day.
- Made by
- OpenAI
- Released
- August 2025
- Kind
- Labs' internal R&D tests
- Level
- Research frontier
- Scored
- Graded by a model
- Answers
- Agent task
- Tasks
- Kept private
- Strongest model tested
- GPT-5.5 #20 on the capability index
- Last in a system card
- April 2026
- Last model added
- April 2026
2 models share the top score of 8.00% among models released by April 2026
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.
The newest model with a score here came out in April 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 15 more
- 1 GPT-5.1-Codex-Max 8.00%
- 2 GPT-5.2 Codex 8.00%
- 3 GPT-5.4 Mini 7.50%
- 4 GPT-5.3 Codex 6.00%
- 5 GPT-5.4 4.16%
- 6 GPT-5.2 3.00%
- 7 GPT-5 2.00%
- 8 GPT-5.5 1.70%
- 9 GPT-5 mini 1.00%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.1-Codex-Max | 8.00% | Nov 2025 | Lab model card | |
| 2 | GPT-5.2 Codex | 8.00% | Dec 2025 | Lab model card | |
| 3 | GPT-5.4 Mini | 7.50% | Mar 2026 | Lab model card | |
| 4 | GPT-5.3 Codex | 6.00% | Feb 2026 | Lab model card | |
| 5 | GPT-5.4 | 4.16% | Mar 2026 | Lab model card | |
| 6 | GPT-5.2 | 3.00% | Dec 2025 | Lab model card | |
| 7 | GPT-5 | 2.00% | Aug 2025 | Lab model card | |
| 8 | GPT-5.5 | 1.70% | Apr 2026 | Lab model card | |
| 9 | GPT-5 mini | 1.00% | Aug 2025 | Lab model card |
2 models have held number one in labs’ own numbers since its release in August 2025
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
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.
9 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 |
|---|---|---|---|---|
| Apr 23, 2026 | GPT-5.5 system card | 1.7 | OpenAI-Proof Q&A, pass@1, printed chart label (Figure 22, 9.1.3.4) | AI R&D risk |
| Mar 17, 2026 | GPT-5.4 Mini system card | 7.5 | OpenAI-Proof Q&A v0 (OPQA), pass@1 (Table 20) | AI R&D risk |
| Mar 5, 2026 | GPT-5.4 system card | 4.16 | OpenAI-Proof Q&A, pass@1, printed chart label (Figure 17, 5.1.3.3) | AI R&D risk |
| Feb 5, 2026 | GPT-5.3 Codex system card | 6 | OpenAI-Proof Q&A, pass@1, printed chart label (Figure 9, 5.1.3.2) | AI R&D risk |
| Dec 18, 2025 | GPT-5.2 Codex system card addendum | 8 | OpenAI-Proof Q&A, pass@1, printed chart label (Figure 12, 5.1.3.4) | AI R&D risk |
| Dec 11, 2025 | GPT-5.2 system card addendum | 3 | OpenAI-Proof Q&A (OPQA), pass@1, no browsing, printed chart label (Figure 16) | AI R&D risk |
| Nov 18, 2025 | GPT-5.1-Codex-Max system card | 8 | OpenAI-Proof Q&A, pass@1, printed chart label (8% also with refusals counted) (Figure 14, 5.1.3.5) | AI R&D risk |
| Aug 7, 2025 | GPT-5 system card | 2 | OpenAI-Proof Q&A, pass@1, no browsing launch candidate (Figure 26) | AI R&D risk |
| Aug 7, 2025 | GPT-5 mini system card | 1 | OpenAI-Proof Q&A, pass@1, gpt-5-thinking-mini launch candidate (Figure 26) | AI R&D risk |
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