GeneBench

GeneBench is an evaluation focused on multi-stage scientific data analysis in genetics and quantitative biology. Tasks require reasoning about ambiguous or noisy data with minimal supervisory guidance, addressing realistic obstacles such as hidden confounders or QC failures, and correctly implementing and interpreting modern statistical methods.

GPT-5.5 Pro from OpenAI currently leads the GeneBench leaderboard with a score of 0.332 across 2 evaluated AI models.

Paper

OpenAIGPT-5.5 Pro leads with 33.2%, followed by OpenAIGPT-5.5 at 25.0%.

Progress Over Time

Interactive timeline showing model performance evolution on GeneBench

State-of-the-art frontier
Open
Proprietary

GeneBench Leaderboard

2 models
ContextCostLicense
1
2
OpenAI
OpenAI
1.1M$5.00 / $30.00
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FAQ

Common questions about GeneBench.

What is the GeneBench benchmark?

GeneBench is an evaluation focused on multi-stage scientific data analysis in genetics and quantitative biology. Tasks require reasoning about ambiguous or noisy data with minimal supervisory guidance, addressing realistic obstacles such as hidden confounders or QC failures, and correctly implementing and interpreting modern statistical methods.

What is the GeneBench leaderboard?

The GeneBench leaderboard ranks 2 AI models based on their performance on this benchmark. Currently, GPT-5.5 Pro by OpenAI leads with a score of 0.332. The average score across all models is 0.291.

What is the highest GeneBench score?

The highest GeneBench score is 0.332, achieved by GPT-5.5 Pro from OpenAI.

How many models are evaluated on GeneBench?

2 models have been evaluated on the GeneBench benchmark, with 0 verified results and 2 self-reported results.

Where can I find the GeneBench paper?

The GeneBench paper is available at https://cdn.openai.com/pdf/6dc7175d-d9e7-4b8d-96b8-48fe5798cd5b/oai_genebench_benchmark.pdf. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does GeneBench cover?

GeneBench is categorized under agents, reasoning, and science. The benchmark evaluates text models.

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