Qwen2.5 VL 7B Instruct vs Qwen3.6 Plus

Compare Qwen2.5 VL 7B Instruct and Qwen3.6 Plus 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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Qwen2.5 VL 7B Instruct vs Qwen3.6 Plus: 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.

Qwen3.6 Plus

Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.

Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.

Qwen2.5 VL 7B Instruct vs Qwen3.6 Plus Comparison Table

PropertyQwen2.5 VL 7B InstructQwen3.6 Plus
OrganizationQwenQwen
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJan 2025Apr 2026
Context Window33K1.0M
Parameters7B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.325
Output $/1M$1.95
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%
68.66%
Avg Response Time47.64s34.17s
Median input tokensincl. image tokens1.2K
Median output tokens47
Est. cost / taskon this benchmark$0.0005
Defect Detection
60%(9/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
57.1%(8/14)
78.6%(11/14)
Spatial Understanding
57.9%(11/19)
68.4%(13/19)
OCR
Overall Score
58.52%
Avg Response Time5.49s
Median input tokensincl. image tokens124
Median output tokens18
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
76.8%(76/99)
Handwritten Math
80%(8/10)
License Plate Recognition
13.3%(4/30)
Text Recognition
50%(15/30)
VQA & Extraction
51.7%(31/60)

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