Qwen2.5 VL 7B Instruct vs YOLO World
Compare Qwen2.5 VL 7B Instruct and YOLO World side-by-side.
Compare Qwen2.5 VL 7B Instruct vs YOLO World live
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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
Qwen2.5 VL 7B Instruct vs YOLO World: Overview
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.
YOLO-World v2 Small (YOLO-World-S-v2) is the smallest variant of Tencent AI Lab’s YOLO-World v2 family, released around February 2024 under GPL-v3. With ~13 million parameters, it adopts a prompt-then-detect paradigm using offline vocabularies and is pretrained on large-scale datasets such as Objects365 and GoldG. The model processes image inputs at 640×640 or 1280×1280 resolutions and supports zero-shot open-vocabulary object detection, enabling recognition of novel categories from text prompts without retraining.
Evaluations show competitive results across benchmarks like LVIS and COCO, while maintaining real-time efficiency. On an NVIDIA V100, the small variant reaches ~74 FPS at standard resolutions. Together with larger YOLO-World v2 models, it provides a scalable framework for efficient, open-vocabulary detection across diverse deployment settings.
Qwen2.5 VL 7B Instruct vs YOLO World Comparison Table
| Property | Qwen2.5 VL 7B Instruct | YOLO World |
|---|---|---|
| Organization | Qwen | Tencent AI Lab |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2025 | Feb 2024 |
| Context Window | 33K | 13.0M |
| Parameters | 7B | |
| License | Apache 2.0 | GPL v3 |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 52.24% | |
| Avg Response Time | 47.64s | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 57.1%(8/14) | |
| Spatial Understanding | 57.9%(11/19) | |