OpenAI Research Engineer Interview: Coding
Models solve 18 self-contained coding problems like those in OpenAI interviews. Scored as pass@1.
In-house OpenAI evaluation from the internal interview question bank.
- Made by
- OpenAI
- Released
- August 2024
- Kind
- Labs' internal R&D tests
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Code
- Tasks
- Kept private
- Strongest model tested
- o3 #83 on the capability index
- Last in a system card
- April 2025
- Last model added
- April 2025
o1 leads with 100% among models released by April 2025
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 2025, 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 16 more
- 1 o1 100%
- 2 o4-mini 98%
- 3 o3 97%
- 4 GPT-4o May 2024 95%
- 5 o3-mini 92%
- 6 OpenAI deep research 79%
- 7 GPT-4.5 79%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | o1 | 100% | Dec 2024 | Lab model card | |
| 2 | o4-mini | 98% | Apr 2025 | Lab model card | |
| 3 | o3 | 97% | Apr 2025 | Lab model card | |
| 4 | GPT-4o May 2024 | 95% | Aug 2024 | Lab model card | |
| 5 | o3-mini | 92% | Jan 2025 | Lab model card | |
| 6 | OpenAI deep research | 79% | Feb 2025 | Lab model card | |
| 7 | GPT-4.5 | 79% | Feb 2025 | Lab model card |
2 models have held number one in labs’ own numbers since its release in August 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
OpenAI’s own number for GPT-4o passed 85% in August 2024
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 16, 2025 | o4-mini system card | 98 | OpenAI RE Interview Coding, pass@1, o4-mini no browsing launch candidate, printed chart label (Figure 17) | AI R&D risk |
| Apr 16, 2025 | o3 system card | 97 | OpenAI RE Interview Coding, pass@1, o3 no browsing launch candidate, printed chart label (Figure 17) | AI R&D risk |
| Feb 27, 2025 | GPT-4.5 system card | 79 | OpenAI Research Engineer Interview, coding questions (4.6.1) | Autonomy |
| Feb 25, 2025 | OpenAI deep research system card | 79 | OpenAI RE Interview Coding, pass@1, post-mitigation with browsing, text and printed chart label (without browsing 86%) (3.4.7) | Autonomy |
| Jan 31, 2025 | o3-mini system card | 92 | OpenAI RE Interview Coding, pass@1, post-mitigation (5.7.1) | Autonomy |
| Dec 5, 2024 | o1 system card | 100 | OpenAI RE Interview Coding, pass@128 as the card reports (pass@1 83%), o1 (post-mitigation), printed chart label (5.8.1) | Autonomy |
| Sep 12, 2024 | o1-preview system card | OpenAI Research Engineer Interviews (Coding), pass@1 and pass@128, chart without printed numbers (4.5.1) | Autonomy | |
| Sep 12, 2024 | o1-mini system card | OpenAI Research Engineer Interviews (Coding), pass@1 and pass@128, chart without printed numbers (4.5.1) | Autonomy | |
| Aug 8, 2024 | GPT-4o system card | 95 | OpenAI research coding interview, pass@100 (3.8) | 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