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 August 20, 2026Pricing updated August 24, 2026
| Property | Qwen3.7 Plus | RF-DETR Segmentation |
|---|---|---|
| Organization | Qwen | Roboflow |
| Category | closed | open |
| Modality | — | vision |
| Release Date | — | Oct 2025 |
| Context Window | — | — |
| Parameters | 33.6M-38.6M | |
| License | Apache 2.0 | |
| Model Sizes input resolution per size variant | ||
| Nano | 312×312 | |
| Small | 384×384 | |
| Medium | 432×432 | |
| Large | 504×504 | |
| XL | 624×624 | |
| 2XL | 768×768 | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.320 | |
| Output $/1M | $1.28 | |
| Vision Tasks | ||
| captioning | Demo | |
| classification | Demo | |
| Instance Segmentation | Demo (COCO) | |
| object-detection | Demo | |
| ocr | Demo | |
| Visual Question Answering | Demo | |
| Model Features | ||
| Real-Time Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 67.4% | Not evaluated |
| Avg cost / sample | $0.0008 | – |
| Avg speed / sample | 7.01s | – |
| By task | ||
| Object Detection | 60.1% $0.0013 | – |
| Counting | 50.0% $0.0004 | – |
| Identification | 84.4% $0.0003 | – |
| OCR | 86.5% $0.0009 | – |
| Data Extraction | 83.5% $0.0004 | – |
| Reasoning (low) | 39.7% $0.0003 | – |
| Reasoning (high) | 68.2% $0.0043 | – |
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.
