GPT-6 Astra tops StationeryBench 7 to 0 over MolmoAct2

StationeryBench is an early, small-scale test, but the gap it shows is stark: across 200 trials on the same dual-arm YAM robots, OpenAI's GPT-6 Astra finished 7 of 100 tasks while Ai2's MolmoAct2 finished none. Astra's median progress score of 46 out of 100, against MolmoAct2's 12, suggests the difference is not only about completing a task but about how far each model gets toward it.

The benchmark itself covers only five desk-object tasks — uncapping a marker, pouring out paper clips, passing a ruler between two robot arms — so it is a narrow slice of manipulation. Still, the results come with videos and code published on GitHub, and they follow OpenAI's stated long-term plans to build its own consumer robots, which makes this an early signal of how its models might perform outside text and images.

Outside researcher Yoav Artzi's description of a "step change in spatial reasoning" rests partly on the still-unpublished REMAP benchmark, where Astra reaches accuracy close to human level. Artzi's own caveat that "even ASTRA doesn't get to what humans do in other scenarios," and his suspicion that OpenAI trained on large amounts of 3D data such as Blender scenes, suggest the outcome hinges on how much of this gain is general versus tied to 3D-heavy training.