SAM 3 vs YOLOv4
Compare SAM 3 and YOLOv4 side-by-side.
Compare SAM 3 vs YOLOv4 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 YOLOv4 Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | SAM 3 | YOLOv4 |
|---|---|---|
| Organization | Meta | Academia Sinica |
| Category | open | open |
| Modality | multimodal | vision |
| Release Date | Nov 2025 | Apr 2020 |
| Context Window | — | — |
| Parameters | Unknown | Unknown |
| License | Custom | Custom |
| Commercial use | See license terms | See license terms |
| Vision Tasks | ||
| Object Detection | Demo | Supported |
| Instance Segmentation | Supported | Not listed |
| Open Vocabulary Object Detection | Supported | Not listed |
| Promptable Concept Segmentation | Demo | Not listed |
| Video Object Tracking | Supported | Not listed |
| Zero Shot Segmentation | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
| Zero-shot Detection | Supported | Not listed |
SAM 3 vs YOLOv4: 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.
YOLOv4 is an object detection model developed by Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao at Academia Sinica, released in April 2020 via the Darknet framework. It combines a CSPDarknet53 backbone, PANet neck, and YOLOv3 detection head with a large set of training improvements — Bag of Freebies and Bag of Specials — that improve accuracy with minimal inference cost increase.
YOLOv4 achieves 43.5% AP on COCO at 65 FPS on a Tesla V100 GPU. The Darknet implementation is the original version, distinguishing it from subsequent PyTorch-based reimplementations. It remains a widely referenced detection architecture and a supported training target in Roboflow Inference.
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