Qwen2.5 VL 7B Instruct vs Qwen3.5 397B A17B

Compare Qwen2.5 VL 7B Instruct and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.

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QwenQwen2.5 VL 7B Instruct
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QwenQwen3.5 397B A17B
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Qwen2.5 VL 7B Instruct vs Qwen3.5 397B A17B: 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 397B A17B

Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.

As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.

Qwen2.5 VL 7B Instruct vs Qwen3.5 397B A17B Comparison Table

PropertyQwen2.5 VL 7B InstructQwen3.5 397B A17B
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJan 2025Feb 2026
Context Window33K262K
Parameters7B397B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.385
Output $/1M$2.45
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%
58.21%
Avg Response Time47.64s56.61s
Median input tokensincl. image tokens1.1K
Median output tokens54
Est. cost / taskon this benchmark$0.0006
Defect Detection
60%(9/15)
66.7%(10/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
57.1%(8/14)
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
57.9%(11/19)
OCR
Overall Score
68.56%
Avg Response Time7.45s
Median input tokensincl. image tokens122
Median output tokens20
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
57.6%(57/99)
Handwritten Math
80%(8/10)
License Plate Recognition
100%(30/30)
Text Recognition
70%(21/30)
VQA & Extraction
68.3%(41/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology