SAM 3 vs YOLOX

Compare SAM 3 and YOLOX side-by-side.

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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

Meta

SAM 3 vs YOLOX: Overview

SAM 3

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.

YOLOX

YOLOX is an anchor-free object detection model developed by Megvii (Face++), released in July 2021 under the Apache 2.0 license. It applies anchor-free detection to the YOLO framework, decoupling the classification and regression heads to allow each to optimize independently, and introduces the SimOTA label assignment strategy for improved training convergence. YOLOX achieves strong accuracy-speed tradeoffs and outperforms YOLOv5 on COCO at comparable model sizes.

YOLOX-L achieves 50.0% AP on COCO at 68.9 FPS on an NVIDIA V100 GPU. The model is available in a range of sizes from YOLOX-Nano to YOLOX-X and supports deployment through ONNX, TensorRT, and other standard export formats. It is suitable for real-time object detection applications and has been widely adopted in industrial and research detection pipelines.

SAM 3 vs YOLOX Comparison Table

PropertySAM 3YOLOX
OrganizationMetaMegvii
Categoryclosedopen
Modalitymultimodalvision
Release DateNov 2025Jul 2021
Context Window
Parameters0.91M-99.1M
LicenseProprietaryApache 2.0
Vision Tasks
Object DetectionDemo
Instance Segmentation
Promptable Concept SegmentationDemo
Video Object Tracking
Zero Shot Segmentation
Model Features
Foundation Vision
Zero-shot Detection