OpenAI’s GPT-6 Astra scores higher on lab tasks but still makes basic errors

OpenAI’s GPT-6 Astra earned 66.7% of the available credit on a new laboratory analysis benchmark, compared with 43.8% for GPT-5.6 Sol, but still made counting errors and selected the wrong experimental samples.

The results come from BioBasicsBench, developed by OpenAI with enzyme design company Ligo Biosciences. It assesses how AI agents interpret evidence from wet labs, where scientists conduct experiments with biological samples.

Both figures were recorded at the “xhigh” reasoning setting, which allows models more reasoning per task. Scores reflect the average credit earned against expert-written grading criteria, including partial credit for elements of an answer that are correct.

The evaluation shows improvements in interpreting images and recognizing experimental problems. It also documents cases where Astra noticed relevant evidence but failed to carry it through to its final answer.

OpenAI cautions: “Higher benchmark scores do not yet mean models can make reliable judgments in the lab.”

Spotting problems that earlier models missed

Several examples show Astra recognizing limits in experimental evidence that GPT-5.6 Sol overlooked.

In one task, GPT-5.6 Sol classified cells as dead based on the brightness of a dye signal. However, the detector could also pick up light from a second dye, making brightness alone insufficient to support that conclusion. Astra identified the ambiguity and called for better controls to separate the signals.