OpenAI Launches LifeSciBench: Expert-Written AI Benchmark for Life Science Research
Summary
- • OpenAI releases LifeSciBench with 750 expert-authored tasks across seven biological domains.
- • 173 Ph.D.-level scientists with biotech and pharma experience created and reviewed all tasks.
- • 79% of tasks require multi-step reasoning; 53% demand artifact interpretation from figures or data files.
- • Benchmark covers seven research workflows from evidence handling to scientific communication.
Details
750 tasks, 173 contributors, 453 reviewers, 19,020 rubric criteria
Largest expert-constructed life science AI benchmark to date, grounded in real biotech and pharma research workflows.
Seven workflow categories
Evidence handling, analysis, design/optimization, scientific reasoning, validation/operations, translation, and scientific communication — all drawn from surveys of practicing life scientists.
79% multi-step tasks (avg 4 steps); 53% require artifact interpretation
Artifacts include figures, PDFs, tables, sequence files, structure/chemical files, and web references — totaling 1,062 attached files.
Expert-authored rubrics with 90%+ reviewer consensus
Each task underwent up to six automated review cycles and at least two expert review rounds before acceptance, anchored in verifiable answers or strong domain consensus.
Grounded in real drug discovery workflows
OpenAI surveyed practicing life scientists to define the benchmark taxonomy, ensuring tasks reflect applied biotech/pharma research rather than academic trivia.
LifeSciBench evaluation framework details
What This Means
OpenAI has released LifeSciBench, a rigorous AI evaluation framework designed by 173 Ph.D.-level life scientists to test whether AI can genuinely assist with complex, real-world scientific research — not just answer textbook biology questions. With 750 multi-step tasks requiring interpretation of actual research artifacts and expert-consensus grading, it sets a significantly higher bar than existing life science benchmarks. This benchmark could become the standard for evaluating AI models intended for drug discovery and biomedical research pipelines, helping identify which AI systems are truly ready to contribute meaningfully to scientific progress.