reasoning benchmark
Internal Research Debugging Evaluation Leaderboard
The Internal Research Debugging Evaluation measures whether models can debug 41 real bugs from internal OpenAI research experiments (plus alignment-auditing tasks), where the original solutions took experienced researchers hours to days. Passing corresponds to providing assistance that would unblock the user, including partial root-cause explanations or fixes.
Updated Aug 17, 2026
Models3
Model coverage3
MetricScore
EvidenceB
Internal Research Debugging Evaluation Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score68.3% | Percentile100.0% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelOP | Score67.8% | Percentile50.0% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelOP | Score50.8% | Percentile0.0% | Participants3 | EvidenceC | Evaluated |
Internal Research Debugging Evaluation Highlights
The leading models and scores on this benchmark.
Internal Research Debugging Evaluation Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Internal Research Debugging Evaluation
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis internal research debugging evaluation AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01OPGPT-5.6 SolOpenAI
- Score
- 68.3%
- Price
- $5.0 input / $30 output per 1M tokens
- Speed
- Up to 27 tok/s via OpenAI
Strengths
- Ranks #1 of 3 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures Internal Research Debugging Evaluation, not total model capability
- 02OPGPT-5.6 TerraOpenAI
- Score
- 67.8%
- Price
- $2.0 input / $12 output per 1M tokens
- Speed
- Up to 35 tok/s via OpenAI
Strengths
- Ranks #2 of 3 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures Internal Research Debugging Evaluation, not total model capability
- 03OPGPT-5.6 LunaOpenAI
- Score
- 50.8%
- Price
- $0.20 input / $1.2 output per 1M tokens
- Speed
- Up to 49 tok/s via OpenAI
Strengths
- Ranks #3 of 3 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures Internal Research Debugging Evaluation, not total model capability
Selection summary
Best AI Models for Internal Research Debugging Evaluation
GPT-5.6 Sol currently leads Internal Research Debugging Evaluation with 68.3%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is Internal Research Debugging Evaluation?
What Internal Research Debugging Evaluation measures and how its scores work.
The Internal Research Debugging Evaluation measures whether models can debug 41 real bugs from internal OpenAI research experiments (plus alignment-auditing tasks), where the original solutions took experienced researchers hours to days. Passing corresponds to providing assistance that would unblock the user, including partial root-cause explanations or fixes.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Internal Research Debugging Evaluation
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- internal-research-debugging-evaluation|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Internal Research Debugging Evaluation.
Which model scores highest on Internal Research Debugging Evaluation?
GPT-5.6 Sol is currently ranked first with 68.3%.
What does Internal Research Debugging Evaluation measure?
The Internal Research Debugging Evaluation measures whether models can debug 41 real bugs from internal OpenAI research experiments (plus alignment-auditing tasks), where the original solutions took experienced researchers hours to days. Passing corresponds to providing assistance that would unblock the user, including partial root-cause explanations or fixes.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.