vision-models object-detection cost-analysis gpt-5-6 benchmark

GPT-5.6 Sol closes gap on vision benchmarks

Sol reaches 46.2 mAP@50 object detection (up from 13.8 in GPT-5.5), but costs 2.5¢/image and fails on images ≥2000×2000px without resizing workaround.

Summary

Vision capabilities now enable practical document workflows, UI automation, and dense-scene detection—but cost and latency tradeoffs mean Gemini 3.5 Flash remains cheaper for high-volume batch processing. Developers building agents must account for 10s latency (Sol) vs 6s (Terra) vs 5s (Luna).

Why it matters

Implementation verdict

Sol replaces GPT-5.5 for object detection and counting; Terra/Luna offer better cost-latency balance. Requires: resize images >2000×2000px before API calls, use XYXY absolute coordinates (not normalized), budget 0.5–2.5¢ per image. Worth testing now for document extraction and screen understanding; skip for cost-sensitive detection at scale.

Sources

1.Sol reached 46.2 mAP@50 in our benchmark, while Sol reached 46.2. Terra and Luna followed closely at 44.7 and 43.3
2.Sol becomes less stable on images around 2,000 by 2,000 pixels or larger, especially at lower reasoning effort
3.Sol cost roughly 2.5 cents per image
4.Sol scored 73.0% in our benchmark, up from 64.9% for GPT-5.5

Dev Signal

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