Qwen2.5 VL 7B Instruct vs Qwen3.5 122B A10B
Compare Qwen2.5 VL 7B Instruct and Qwen3.5 122B A10B 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 122B A10B Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Qwen2.5 VL 7B Instruct | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2025 | Feb 2026 |
| Context Window | 33K | 256K |
| Parameters | 7B | 122B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.290 | |
| Output $/1M | $2.40 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Qwen2.5 VL 7B Instruct vs Qwen3.5 122B A10B: 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.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.