GPT-6 Astra Is the Best Vision Model We Have Tested

Roboflow’s evaluation finds GPT-6 Astra to be its strongest tested vision-language model, leading benchmarks in object detection, counting, and visual reasoning while supporting text-based and example-based box prompting, segmentation, video re-identification, and robot-control experiments. Astra achieved 82.1% mAP@50 for detection at low reasoning effort, with notable performance on fine-grained categories and context-dependent concepts, and it can use positive and negative visual examples to substantially improve results or transfer targets across images. Although its language understanding helps it identify and label regions more accurately than SAM 3 in some segmentation tasks, SAM 3 produces more precise boundaries, making a combined Astra-plus-SAM workflow potentially preferable. In video, Astra maintained player identities, assigned teams, and read jersey numbers across sampled basketball footage, but its per-frame cost makes it better suited to periodic high-level interpretation alongside local detectors and trackers. Robot demonstrations suggested that Astra can use visual feedback to guide actions, though outcomes remain highly dependent on calibration, controllers, and physical setup. Despite its performance, Astra is expensive and relatively slow, with higher reasoning effort offering modest gains at substantially greater cost, so Roboflow recommends testing cheaper models first and validating Astra on task-specific datasets.

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