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# SAM 3 alternatives

> Benchmark comparisons, capability matrices, and deployment trade-offs for SAM 3 and its leading alternatives.

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  |

## SAM 3 vs YOLO, FastSAM, and RF-DETR

| 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 matrix

| 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 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.

## Sources

1. [DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding (arXiv:2411.14347)](https://arxiv.org/abs/2411.14347). Reference for DINO-X.
2. [Gemini 2.5 Technical Report, Google DeepMind](https://storage.googleapis.com/deepmind-media/gemini/gemini_v2_5_report.pdf). CountBench and PixMo-Count scores are from the SAM 3 paper.
3. [Introducing SAM 3, Meta AI Blog](https://ai.meta.com/blog/segment-anything-model-3/). 30 ms per-image latency on H200 with 100+ objects.
4. [Molmo and PixMo, Deitke et al. (arXiv:2409.17146)](https://arxiv.org/abs/2409.17146). PixMo-Count originates from this work.
5. [Qwen2-VL, Wang et al. (arXiv:2409.12191)](https://arxiv.org/abs/2409.12191).
6. [RF-DETR Segmentation, Roboflow Blog](https://blog.roboflow.com/rf-detr-segmentation-preview/). COCO mask mAP and 4.4 ms latency.
7. [SAM 3: Segment Anything with Concepts, Carion et al. (arXiv:2511.16719)](https://arxiv.org/abs/2511.16719). LVIS zero-shot mask AP, counting benchmarks, H200 latency, SAM 3 Agent.
8. [YOLO11 Documentation, Ultralytics](https://docs.ultralytics.com/models/yolo11/). YOLO11n-seg 1.8 ms on T4 TensorRT10, 32.0 COCO mask mAP.
9. [YOLOv12 GitHub, sunsmarterjie](https://github.com/sunsmarterjie/yolov12). YOLOv12-N 1.64 ms, 40.6 COCO box mAP.