Compare SAM 3 vs YOLOE 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.
SAM 3 vs YOLOE Comparison Table
| Property | SAM 3 | YOLOE |
|---|---|---|
| Organization | Meta | THU-MIG |
| Category | open | open |
| Modality | multimodal | vision |
| Release Date | Nov 2025 | Mar 2025 |
| Context Window | — | — |
| Parameters | Unknown | 10M-50M |
| License | Custom | AGPL 3.0 |
| Commercial use | See license terms | commercial use needs a license (included on Roboflow cloud) |
| Vision Tasks | ||
| Object Detection | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Video Object Tracking | Supported | Not listed |
| Zero Shot Segmentation | Supported | Not listed |
Both support 2 more tasks | ||
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
| Real-Time Vision | Not listed | Supported |
Both support 1 more feature | ||
SAM 3 vs YOLOE: 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.
YOLOE (YOLO with Everything) is an open-vocabulary object detection and segmentation model developed by THU-MIG at Tsinghua University, released in March 2025 under the AGPL-3.0 license. It extends the YOLO architecture to support open-vocabulary detection through text and visual prompts, enabling the model to detect arbitrary object categories beyond a fixed training set without retraining. The design integrates prompt encoding directly into the YOLO framework while preserving real-time inference speed.
YOLOE is evaluated on COCO and LVIS benchmarks and supports both closed-set and open-vocabulary detection modes. It is built on the Ultralytics codebase and maintains compatibility with standard YOLO training and export workflows. YOLOE is suited for applications requiring flexible, prompt-driven object detection where the target object vocabulary may change at inference time.
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