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.
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Qwen2.5 VL 7B Instruct vs Qwen3.5 9b: 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.
Qwen3.5-9B 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
| Property | Qwen2.5 VL 7B Instruct | Qwen3.5 9b |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2025 | Mar 2026 |
| Context Window | 33K | 262K |
| Parameters | 7B | 9B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | |
| Output $/1M | $0.150 | |
| 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% | 71.64% |
| Avg Response Time | 47.64s | 8.99s |
| 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) |