OpenAI's GeneBench-Pro Puts AI Agents to the Test in Computational Biology

OpenAI has introduced GeneBench-Pro, a research benchmark designed to assess whether AI agents can perform the complex, judgment-intensive analysis required in real-world computational biology. Unlike conventional benchmarks focused on factual recall, GeneBench-Pro measures what OpenAI calls "research taste" — the sequence of judgment calls in scientific analysis, from interpreting ambiguous data to deciding whether findings are robust enough to inform downstream research. The benchmark comprises 129 problems spanning ten domains, including statistical genetics, cancer genomics, clinical diagnostics, and pharmacogenomics. Reviewers estimated each task would require 20 to 40 hours of work by a human expert — at an estimated $200 per hour — compared with AI inference costs of just a few dollars per task. OpenAI's GPT-5.6 Sol model achieved a pass rate of just 28.7%, rising to 31.5% in Pro mode — still a significant leap from GPT-5's sub-5% score on the original GeneBench. To encourage independent evaluation, OpenAI is open-sourcing ten tasks on Hugging Face and providing a 50-question subset to Artificial Analysis for third-party benchmarking.

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