Roboflow

Qwen2.5 VL 7B Instruct vs Qwen3.5 9b

Compare Qwen2.5 VL 7B Instruct and Qwen3.5 9b side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.

Compare Qwen2.5 VL 7B Instruct vs Qwen3.5 9b live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
QwenQwen2.5 VL 7B Instruct
Run to compare this model.
QwenQwen3.5 9b
Run to compare this model.

Models in this comparison

Qwen2.5 VL 7B Instruct vs Qwen3.5 9b: 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.

Qwen3.5 9b

Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.

The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.

Qwen2.5 VL 7B Instruct vs Qwen3.5 9b Comparison Table

PropertyQwen2.5 VL 7B InstructQwen3.5 9b
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJan 2025Mar 2026
Context Window33K262K
Parameters7B9B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.100
Output $/1M$0.150
Vision Tasks
CaptioningDemoDemo
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
52.24%
71.64%
Avg Response Time47.64s8.99s
Defect Detection
60%(9/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
66.7%(6/9)
Object Counting
0%(0/10)
30%(3/10)
Object Understanding
57.1%(8/14)
71.4%(10/14)
Spatial Understanding
57.9%(11/19)
84.2%(16/19)