Qwen3.7 Plus vs RF-DETR Segmentation
Compare Qwen3.7 Plus and RF-DETR Segmentation side-by-side.
Compare Qwen3.7 Plus vs RF-DETR Segmentation 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
Qwen3.7 Plus vs RF-DETR Segmentation Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Qwen3.7 Plus | RF-DETR Segmentation |
|---|---|---|
| Organization | Qwen | Roboflow |
| Category | closed | open |
| Modality | — | vision |
| Release Date | Jun 2026 | Oct 2025 |
| Context Window | — | — |
| Parameters | Unknown | 33.6M-38.6M |
| License | Unknown | Apache 2.0 |
| Model Sizes input resolution per size variant | ||
| Nano | Not available | 312×312 |
| Small | Not available | 384×384 |
| Medium | Not available | 432×432 |
| Large | Not available | 504×504 |
| XL | Not available | 624×624 |
| 2XL | Not available | 768×768 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.320 | No published price |
| Output $/1M | $1.28 | No published price |
| Vision Tasks | ||
| captioning | Demo | Not listed |
| classification | Demo | Not listed |
| Instance Segmentation | Not listed | Demo (COCO) |
| object-detection | Demo | Not listed |
| ocr | Demo | Not listed |
| Visual Question Answering | Demo | Not listed |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 58.9% | Not evaluated |
| Avg cost / sample | $0.0008 | – |
| Avg speed / sample | 7.77s | – |
| By task | ||
| Object Detection | 60.1% | – |
| Counting | 50.0% | – |
| Identification | 84.4% | – |
| OCR (low) | 60.3% | – |
| by category |
| |
| OCR (high) | 65.5% | – |
| by category |
| |
| Reasoning (low) | 39.7% | – |
| Reasoning (high) | 68.2% | – |
Qwen3.7 Plus vs RF-DETR Segmentation: Overview
RF-DETR Segmentation is a real-time instance segmentation model developed by Roboflow, with a preview base model released in October 2025 under the Apache 2.0 license and the full variant family — Nano through 2XL — released in January 2026. It extends the RF-DETR object detection architecture with a segmentation head inspired by MaskDINO, enabling pixel-level object delineation while maintaining the real-time performance characteristics of the base model. It is deployable through Roboflow Inference and the open-source rfdetr Python package.
RF-DETR Segmentation supports fine-tuning on custom COCO- or YOLO-format instance segmentation datasets and is benchmarked on Microsoft COCO. It is suited for applications requiring both precise object masks and real-time inference, such as robotic manipulation, quality control, and augmented reality overlays.