SAM 3 vs YOLO26
Compare SAM 3 and YOLO26 side-by-side.
Compare SAM 3 vs YOLO26 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
SAM 3 vs YOLO26 Comparison Table
Evals updated August 6, 2026Pricing updated August 7, 2026
| Property | SAM 3 | YOLO26 |
|---|---|---|
| Organization | Meta | Ultralytics |
| Category | open | open |
| Modality | multimodal | vision |
| Release Date | Nov 2025 | Oct 2025 |
| Context Window | — | — |
| Parameters | 2.4M-55.7M | |
| License | Custom | AGPL 3.0 |
| Model Sizes input resolution per size variant | ||
| Nano | 640×640 | |
| Small | 640×640 | |
| Medium | 640×640 | |
| Large | 640×640 | |
| XL | 640×640 | |
| Extra Large | 640×640 | |
| Vision Tasks | ||
| Instance Segmentation | Demo (COCO) | |
| Object Detection | Demo | Demo (COCO) |
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| Zero-shot Detection | ||
SAM 3 vs YOLO26: Overview
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
YOLO26 is a real-time object detection model developed by Ultralytics, released in October 2025. It introduces a native end-to-end, NMS-free architecture that eliminates the Non-Maximum Suppression post-processing step, reducing CPU latency by up to 43% for the Nano variant compared to NMS-dependent versions. The model incorporates the MuSGD optimizer and ProgLoss with STAL for improved training stability and small-object detection, and removes Distribution Focal Loss to ensure maximum compatibility with ONNX and TensorRT export targets.
YOLO26 supports object detection, instance segmentation, pose estimation, and oriented bounding box detection within a unified framework, with model sizes available from Nano to Extra Large. Its NMS-free design makes it particularly well suited for deployment scenarios where post-processing overhead is a bottleneck, such as embedded systems and real-time edge inference pipelines.
