AI Weekly Malaysia

Roboflow benchmarked OpenAI's GPT-5.6 lineup (Sol, Terra, Luna) on vision tasks including detection, counting, OCR, and data extraction. Sol scored 46.2 mAP@50 on object detection versus GPT-5.5's 13.8, turning detection from a weakness into a practical capability, though the model sometimes hallucinated boxes in unnatural grid layouts.

Why: If you're building vision pipelines with GPT-5.6, prompt for absolute XYXY coordinates in image pixels—using the wrong format (e.g., Gemini-style normalized YXYX) costs ~15 mAP points. Sol is now viable for document layout detection and dense-scene counting, but expect occasional phantom boxes with no ground-truth overlap, so you still need post-processing validation.