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.
Compare Qwen2.5 VL 7B Instruct vs Qwen3.6 Plus 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.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Qwen2.5 VL 7B Instruct vs Qwen3.6 Plus: 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.
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
| Property | Qwen2.5 VL 7B Instruct | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jan 2025 | Apr 2026 |
| Context Window | 33K | 1.0M |
| Parameters | 7B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.325 | |
| Output $/1M | $1.95 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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 Time | 47.64s | 34.17s |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 47 | |
| 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 Time | 5.49s | |
| Median input tokensincl. image tokens | 124 | |
| Median output tokens | 18 | |
| 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