multimodal benchmark
Hard negative protein binding prediction is an OpenAI evaluation of whether a model can identify true binders among structurally plausible non-binders using 3D complex structures plus tool access.
Updated Oct 23, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score3.13% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What Hard negative protein binding prediction measures and how its scores work.
A multimodal benchmark for identifying true binders among structurally plausible non-binders from 3D complex structures with tool access.
It measures this identification ability using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Hard negative protein binding prediction.
GPT-Rosalind is currently ranked first with 3.13%.
The current leaders are GPT-Rosalind (3.13%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures this identification ability using the Score metric, reported as a ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis hard negative protein binding prediction AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
GPT-Rosalind currently leads Hard negative protein binding prediction with 3.13%. 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.