Qwen3.7 Plus vs RF-DETR
Compare Qwen3.7 Plus and RF-DETR side-by-side.
Compare Qwen3.7 Plus vs RF-DETR 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 Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Qwen3.7 Plus | RF-DETR |
|---|---|---|
| Organization | Qwen | Roboflow |
| Category | closed | open |
| Modality | — | vision |
| Release Date | — | Mar 2025 |
| Context Window | — | — |
| Parameters | 30.5M-126.9M | |
| License | Apache 2.0 | |
| Model Sizes input resolution per size variant | ||
| Nano | 384×384 | |
| Small | 512×512 | |
| Medium | 576×576 | |
| Large | 704×704 | |
| XL | 700×700 | |
| 2XL | 880×880 | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.320 | |
| Output $/1M | $1.28 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo (COCO) |
| captioning | Demo | |
| classification | 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: Overview
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025 under the Apache 2.0 license. It is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark, 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.
