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: Overview

Qwen2.5 VL 7B Instruct

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.

SAM 3

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.

Qwen2.5 VL 7B Instruct vs SAM 3 Comparison Table

PropertyQwen2.5 VL 7B InstructSAM 3
OrganizationQwenMeta
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJan 2025Nov 2025
Context Window33K
Parameters7B
LicenseApache 2.0Proprietary
Vision Tasks
Object DetectionDemo
CaptioningDemo
Instance Segmentation
OCRDemo
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question AnsweringDemo
Zero Shot Segmentation
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
52.24%
Avg Response Time47.64s
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)