GPT 5.6 Sol is the best "vision" model OpenAI ever released
Points and comments are a snapshot, not live.
OpenAI's GPT-5.6 Sol shows major vision gains but trails Gemini on cost and detection.
Roboflow benchmarked GPT-5.6 Sol, Terra, and Luna on object detection, counting, and OCR. Sol achieved 46.2 mAP@50 in detection, up from GPT-5.5's 13.8. Counting accuracy reached 73.0%, up from 64.9%. OCR similarity stayed near 90.7%, slightly below GPT-5.5's 91.2%. Sol costs ~2.5 cents per image and averages 10 seconds. Gemini 3.5 Flash leads benchmarks at 0.8 cents per image. Sol struggles with images over 2000x2000 pixels and some text extraction tasks.
Luna offers the best latency-quality balance at <0.5 cents per image and ~5 seconds. The models require absolute XYXY pixel coordinates for best detection results.
What commenters are saying
Commenters had mixed views. One praised Sol's ability to restructure UI screenshots holistically, claiming Claude over-focuses on one part. Another argued that asking an LLM to assess subjective quality is flawed, though several countered that objective design principles exist. A design system engineer noted LLMs can follow documented design systems. Multiple users shared failures: one found Sol constantly wrong on image outpainting despite $100 Pro access; another believed a coin detection failure was an EXIF rotation issue, not resolution. The blog author said the issue was large image resolution, not EXIF.