SAM3 Alternatives
SAM 3 leads in open-vocabulary segmentation quality, but it's not the only option and not always the right one. This page compares SAM 3 against frontier VLLMs and popular non-VLLM models across benchmarks, latency, capabilities, and deployment fit.
VLLM Benchmark Results
Counting and localisation on CountBench and PixMo-Count. MAE is lower-is-better; accuracy is higher-is-better. Color reflects rank within each row — green is best, red is weakest.
| 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 |
SAM 3 vs YOLO, FastSAM, and RF-DETR
The models teams most commonly evaluate for segmentation pipelines — compared across speed, accuracy, prompting approach, and deployment fit.
| 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 & 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 Matrix
Feature availability at a glance — useful for identifying where a model natively covers a workflow versus where custom engineering is needed.
| 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 |
When to Use SAM 3
SAM 3 is the strongest model for open-vocabulary perception tasks, built for accuracy first. The right strategy depends on where in the pipeline the work happens:
- Dataset construction & auto-labelling — Zero-shot mask quality is high enough to use directly as training labels without domain fine-tuning.
- Interactive annotation — Point and box prompting with real-time mask preview makes SAM 3 the best tool for human-in-the-loop labelling workflows.
- Production edge inference — Use SAM 3 to generate labelled data, then fine-tune YOLO or RF-DETR for sub-5ms edge serving.
- Instruction-driven workflows — SAM 3 Agent decomposes complex queries into prompts and calls SAM 3 iteratively, beating prior work on ReasonSeg and OmniLabel out of the box.
Sources
- [1]DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding (arXiv:2411.14347) — Reference for DINO-X model.
- [2]Gemini 2.5 Technical Report — Google DeepMind — Reference for Gemini 2.5 Pro model. CountBench and PixMo-Count scores are from the SAM 3 paper.
- [3]Introducing SAM 3 — Meta AI Blog — 30 ms per-image latency on H200 with 100+ objects.
- [4]Molmo and PixMo: Open Weights and Open Data for State-of-the-Art VLMs — Deitke et al. (arXiv:2409.17146) — Reference for Molmo-72B model. PixMo-Count benchmark originates from this work.
- [5]Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution — Wang et al. (arXiv:2409.12191) — Reference for Qwen2-VL-72B model.
- [6]RF-DETR Segmentation — Roboflow Blog — RF-DETR Seg COCO mask mAP 50-95 and 4.4 ms latency figures.
- [7]SAM 3: Segment Anything with Concepts — Carion et al., Meta (arXiv:2511.16719, Nov 2025) — LVIS zero-shot mask AP, CountBench / PixMo-Count benchmarks, H200 latency, SAM 3 Agent results on ReasonSeg and OmniLabel.
- [8]YOLO11 Documentation — Ultralytics — YOLO11n-seg: 1.8 ms latency on T4 TensorRT10, 32.0 COCO mask mAP 50-95.
- [9]YOLOv12 GitHub Repository — sunsmarterjie — YOLOv12-N: 1.64 ms latency, 40.6 COCO box mAP 50-95.