OpenAI Introduces BioBasicsBench to Test AI Agents on Wet-Lab Data

OpenAI introduced BioBasicsBench on November 6, 2026, a 100-task benchmark developed with enzyme-design company Ligo to test whether AI agents can interpret raw evidence from routine wet-lab experiments. OpenAI reports results as mean reward, the average task score expressed as a percentage of available credit. At xhigh, the highest reasoning-effort setting reported for all seven models tested, GPT-6 Astra reached 66.7% with a 95% confidence interval of 60.3–73.1%, while GPT-5.6 Sol scored 43.8% with an interval of 37.6–50.0%. Astra averaged 4,704 output tokens per task, compared with 9,099 for Sol. BioBasicsBench addresses a limitation OpenAI identifies in evaluations built around charts and images selected for scientific papers: everyday lab work also involves failed experiments, ambiguous images, conflicting observations and troubleshooting.