RF-DETR vs YOLO vs Cloud API: Which Should You Actually Use in 2026?

RF-DETR just became the first real-time model to break 60 mAP on COCO. The AI community is calling it the end of YOLO's decade-long dominance. But should you actually care? If you're building a production app that detects objects in images, you have three options: RF-DETR locally, YOLO locally, or a Cloud API. We tested all three on the same image. Here are the real numbers. The Test One image, three approaches, same machine (Intel CPU, no GPU). Metric RF-DETR (CPU) YOLOv11 nano (CPU) Cloud API Inference time 1.34s 0.34s 0.65s (incl. network) Objects detected 3 2 6 Top confidence 95.4% 93.6% 97.6% Model size 355MB 5.4MB N/A GPU required Recommended No No The Cloud API detected 6 objects (persons, car, wheel, shoe, hat) while RF-DETR found 3 and YOLO found 2. YOLO missed the car entirely. The Code RF-DETR from rfdetr import RFDETRBase from rfdetr.assets.coco_classes import COCO_CLASSES model = RFDETRBase(device="cpu") detections = model.predict("street.jpg", threshold=0.3) for cls_id, c