Auto Label with GPT-6 Astra in Roboflow

Roboflow’s Auto Label feature uses GPT-6 Astra as its default zero-shot object detection model, allowing users to provide clear class names and automatically generate bounding boxes without training examples or manual annotation. The workflow can be completed through Roboflow’s interface by importing raw images, creating an object detection project, entering classes such as hard hat, mask, and gloves, running a preview, and applying labels across a batch, or through the Roboflow MCP server, where compatible AI agents can initiate the same process from a natural-language prompt. Astra relies only on class names rather than descriptions, returns all detections without confidence filtering, and produces boxes rather than segmentation masks, making human review essential to remove duplicate or inaccurate boxes, adjust loose annotations, correct classes, and add missed objects. Once reviewed and approved, labeled images can be added to a dataset for versioning, training, or export. Compared with Gemini and SAM 3, Astra is positioned as a contextual, reasoning-oriented option for detection tasks, while other models may be more suitable for multimodal analysis or precise segmentation.

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