LifeSciBench turns 750 expert biology tasks into an AI test bed

Life-science AI is moving from trivia-style testing toward work that looks closer to the lab and research desk. OpenAI wrote on X that LifeSciBench is designed to measure how well AI supports “real-world life science research.” The benchmark’s center of gravity is concrete: 173 scientists from biotechnology and pharmaceutical research contributed 750 expert-authored tasks across seven biological research workflows.

OpenAI’s account is usually reserved for official model, product, and research updates, so this post matters less as a social-media update than as evidence of where the company wants evaluation to go. Biology research often requires chaining literature review, hypothesis formation, assay design, protocol reasoning, and interpretation of noisy results. A benchmark split across seven workflows can expose whether a model is broadly useful or merely strong on narrow question-answering formats.

The linked OpenAI page was not accessible to this crawler because it required JavaScript and cookies, so the factual base here is the public tweet and FxTwitter metadata. That still gives enough signal to separate this from ordinary marketing: the tweet names the number of scientists, the number of tasks, and the workflow structure. For researchers, the next question is whether LifeSciBench will publish enough task and scoring detail for third-party replication, and whether model comparisons will show domain-specific failure modes rather than a single leaderboard number. The source tweet is available on X.