SAM 3 alternatives | SegmentationAPI

Compare SAM 3 with frontier VLLMs and popular non-VLLM models across benchmarks, latency, capabilities, and deployment fit. Updated March 3, 2026.

VLLM benchmark results

Counting and localisation on CountBench and PixMo-Count. MAE is lower-is-better. Accuracy is higher-is-better.

Benchmark	SAM 3	Gemini 2.5 Pro	Qwen2-VL-72B	Molmo-72B	DINO-X
CountBench MAE	0.12	0.24	0.28	0.27	0.62
CountBench Accuracy (%)	93.8	92.4	86.7	92.4	82.9
PixMo-Count MAE	0.21	0.38	0.61	0.17	0.21
PixMo-Count Accuracy (%)	86.2	78.2	63.7	88.8	85.0
Average Accuracy (%)	90.0	85.3	75.2	90.6	84.0
Average MAE	0.165	0.310	0.445	0.220	0.415

Dimension	SAM 3	SAM 2	YOLO11-seg	FastSAM	RF-DETR Seg	YOLOv12-N
Latency (ms / image)	~30 ms	~11 ms	~1.8 ms	~8 ms	~4.4 ms	~1.6 ms
Accuracy	48.8 mask AP (LVIS, zero-shot)	44.7 mask AP (LVIS)	32.0 mask mAP (COCO)	~37 mask mAP (COCO)	43.1 mask mAP (COCO)	40.6 box mAP (COCO)
Prompt types	Point, box, text phrase	Point, box	Class label	Point, box, text	Class label	Class label
Open-vocabulary	Yes	Limited	No	Limited	No	No
Best fit	Zero-shot annotation and concept discovery	Interactive video segmentation	Edge segmentation	CPU-constrained prompting	Accurate real-time detection	Ultra-fast detection
Main tradeoff	Slow on edge hardware	Weaker concept segmentation	No open-vocabulary support	Lower quality ceiling	Higher runtime complexity	Detection only, no masks

Capability	SAM 3	SAM 2	YOLO11-seg	FastSAM	RF-DETR	Gemini 2.5
Zero-shot concept segmentation from noun phrases	Yes	No	No	Partial	No	Partial
Per-instance segmentation masks	Yes	Yes	Yes	Yes	Yes	No
Unified image + video detector/tracker	Yes	Memory-bank tracker	No	No	No	No
Interactive refinement (points/boxes)	Yes	Yes	No	Yes	No	No
Long instruction reasoning (no external agent)	Weak	Weak	No	No	No	Strong

SAM 3 is built for accuracy on open-vocabulary perception, not for the fastest edge box.

Dataset construction and auto-labelling: zero-shot mask quality is high enough to use as training labels without domain fine-tuning.
Interactive annotation: point and box prompting with a live mask preview.
Production edge inference: generate labelled data with SAM 3, then fine-tune YOLO or RF-DETR for sub-5ms serving.
Instruction-driven workflows: SAM 3 Agent decomposes complex queries into prompts and calls SAM 3 iteratively.