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reasoning benchmark

GeneBench-Pro

GeneBench-Pro is a research-level benchmark of 129 multi-stage computational-biology problems spanning genomics, quantitative biology, and translational biomedicine. Each problem gives the agent a messy dataset, brief context, and a target estimand, and requires navigating dependent inferential decision points to reach a verifiable answer.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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  • Highlights
  • About
  • FAQ

GeneBench-Pro Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01OPGPT-5.6 SolOpenAI28.7%100.0%3CAug 11, 2026
02OPGPT-5.6 TerraOpenAI23.3%50.0%3CAug 11, 2026
03OPGPT-5.6 LunaOpenAI10.8%0.0%3CAug 11, 2026

GeneBench-Pro Score Distribution

A closer view of the leading scores on this benchmark.

GeneBench-Pro

GeneBench-Pro Highlights

The leading models and scores on this benchmark.

Rank #1GPT-5.6 Sol28.7%Rank #2GPT-5.6 Terra23.3%Rank #3GPT-5.6 Luna10.8%

What is GeneBench-Pro?

What GeneBench-Pro measures and how its scores work.

GeneBench-Pro is a research-level benchmark of 129 multi-stage computational-biology problems spanning genomics, quantitative biology, and translational biomedicine. Each problem gives the agent a messy dataset, brief context, and a target estimand, and requires navigating dependent inferential decision points to reach a verifiable answer.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
GeneBench-Pro
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
genebench-pro|llm-stats-current

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

FAQ

Common questions about GeneBench-Pro.

Which model scores highest on GeneBench-Pro?

GPT-5.6 Sol is currently ranked first with 28.7%.

What does GeneBench-Pro measure?

GeneBench-Pro is a research-level benchmark of 129 multi-stage computational-biology problems spanning genomics, quantitative biology, and translational biomedicine. Each problem gives the agent a messy dataset, brief context, and a target estimand, and requires navigating dependent inferential decision points to reach a verifiable answer.

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.