Qwen2.5 VL 7B Instruct vs SAM 3
Compare Qwen2.5 VL 7B Instruct and SAM 3 side-by-side.
Compare Qwen2.5 VL 7B Instruct 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.
Models in this comparison
Qwen2.5 VL 7B Instruct vs SAM 3 Comparison Table
Evals updated August 6, 2026Pricing updated August 7, 2026
| Property | Qwen2.5 VL 7B Instruct | SAM 3 |
|---|---|---|
| Organization | Qwen | Meta |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2025 | Nov 2025 |
| Context Window | 33K | — |
| Parameters | 7B | |
| License | Apache 2.0 | Custom |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Qwen2.5 VL 7B Instruct vs SAM 3: 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.
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.
