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multimodal benchmark

Hard negative protein binding prediction Leaderboard

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

Models1
Model coverage1
MetricScore
EvidenceB

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Hard negative protein binding prediction Ranking

Higher score ranks better on this benchmark.

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelOPGPT-RosalindOpenAIScore3.13%Percentile100.00%Participants1EvidenceCEvaluatedOct 22, 2026

Hard negative protein binding prediction Highlights

The leading models and scores on this benchmark.

Rank #1GPT-Rosalind3.13%

The Top AI Models for Hard negative protein binding prediction

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

What is Hard negative protein binding prediction?

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.

Family
Hard negative protein binding prediction
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Hard negative protein binding prediction.

Which model scores highest on Hard negative protein binding prediction?

GPT-Rosalind is currently ranked first with 3.13%.

What are the top three models on Hard negative protein binding prediction?

The current leaders are GPT-Rosalind (3.13%).

Which Hard negative protein binding prediction model has the lowest official input price?

No matched official input price is currently available.

Which models are fastest among Hard negative protein binding prediction results?

No matched runtime record is currently available.

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does Hard negative protein binding prediction measure?

It measures this identification ability using the Score metric, reported as a ratio.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

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.

  1. 01
    OP
    GPT-RosalindOpenAI
    Score
    3.13%

    Strengths

    • Ranks #1 of 1 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Hard negative protein binding prediction, not total model capability

Selection summary

Best AI Models for Hard negative protein binding prediction

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.

Benchmark rank #1GPT-Rosalind3.13%