Compare RF-DETR vs SAM 3 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.
RF-DETR vs SAM 3 Comparison Table
| Property | RF-DETR | SAM 3 |
|---|---|---|
| Organization | Roboflow | Meta |
| Category | open | open |
| Modality | vision | multimodal |
| Release Date | Mar 2025 | Nov 2025 |
| Context Window | — | — |
| Parameters | 30.5M-126.9M | Unknown |
| License | Apache 2.0 | Custom |
| Commercial use | free for commercial use (XL and 2XL: PML 1.0, included on Roboflow cloud) | See license terms |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 59.92XL · 126.9M params | Not benchmarked |
| Smallest size | 48.4Nano · 30.5M params | Not benchmarked |
| mAP range | 48.4–59.9across 6 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 48.4384×384 · 30.5M params | Not available |
| Small | 53.0512×512 · 32.1M params | Not available |
| Medium | 54.8576×576 · 33.7M params | Not available |
| Large | 56.3704×704 · 33.9M params | Not available |
| XL | 58.5700×700 · 126.4M params | Not available |
| 2XL | 59.9880×880 · 126.9M params | Not available |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. | ||
| Vision Tasks | ||
| Object Detection | Demo (COCO) | Demo |
| Instance Segmentation | Not listed | Supported |
| Open Vocabulary Object Detection | Not listed | Supported |
| Promptable Concept Segmentation | Not listed | Demo |
| Video Object Tracking | Not listed | Supported |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| Multimodal Vision | Not listed | Supported |
| Real-Time Vision | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
RF-DETR vs SAM 3: Overview
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025. Its largest size, 2XL, reaches 59.9 mAP 50:95 on Microsoft COCO val2017, the highest of the 60 detectors on leaderboard.roboflow.com. It is built on a DINOv2 vision transformer backbone, with weight-sharing neural architecture search used to identify accuracy-latency trade-offs. The full family spans six sizes from Nano (30.5M parameters, 384×384 input) to 2XL (126.9M parameters, 880×880 input), with the accompanying research paper accepted to ICLR 2026.
RF-DETR is designed for strong domain adaptability, achieving state-of-the-art performance on RF100-VL, a benchmark measuring generalization to real-world object detection tasks across diverse domains. It is deployable through Roboflow Inference and supports fine-tuning on custom datasets, making it well suited for domain-specific applications with limited training data.
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.
