OpenAI releases BioBasicsBench for everyday wet-lab analysis

Summary

OpenAI has released BioBasicsBench, a benchmark built with Ligo, an enzyme-design biotech company, to measure how well AI agents handle routine wet-lab analysis. Scientists wrote its 100 tasks and grading rubrics from raw experimental evidence such as gel images, microscopy stacks, and sequencing reads. On the company's own evaluation, GPT-6 Astra earned an average of 66.7% of available credit at the highest reported reasoning setting, well ahead of GPT-5.6 Sol at 43.8%; failure cases, including a roughly 12% overcount in colony counting, were published alongside the results.

Why it matters for our work

How accurately AI reads messy raw experimental data, not polished paper figures, is the crux of automating research support. Publishing benchmark results together with failures gives labs a yardstick for deciding which analysis tasks can be delegated.

Translated from the Korean original. Summaries may be translated and edited. Commentary reflects our perspective; forecasts remain the source’s views.