GPT-6 Astra Is the Best Vision Model We Have Tested
8 hours ago
- Astra is the strongest vision model tested by Roboflow, excelling in object detection, segmentation, counting, visual reasoning, and re-identification.
- In object detection, Astra scores 82.1% mAP@50 at low effort, outperforming Qwen3.8 Max and GPT-5.6 Sol, and can distinguish fine details like LEGO bricks with 99.8% mAP@50.
- Astra supports text and box prompting, allowing it to detect objects from class names or visual examples, and can transfer box prompts between images.
- For segmentation, Astra understands language better than SAM 3 but produces less precise masks; combining Astra's detection with SAM 3's masks yields best results.
- Astra achieves 80.2% counting accuracy and 87.2% visual reasoning accuracy at low effort, improving with higher effort, and can re-identify objects across video frames.
- Astra can connect visual reasoning to robot control actions, as shown in painting and drawing experiments, though physical setup remains critical.
- Astra is expensive and slow ($0.05–$0.08 per image, 11–32 seconds per image), so cheaper models like Qwen3.8 Max may be more cost-effective for many datasets.
- Practical recommendations include starting with low effort, using Astra for auto-annotation when accuracy matters, and combining it with dedicated detectors and trackers for video.