Qwen3.5 397B A17B vs Qwen3 VL 235B A22B Instruct
Compare Qwen3.5 397B A17B and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Qwen3.5 397B A17B vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated August 12, 2026Pricing updated August 13, 2026
| Property | Qwen3.5 397B A17B | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2025 |
| Context Window | 262K | 256K |
| Parameters | 397B | 235B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.260 |
| Output $/1M | $3.60 | $1.04 |
| 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 | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 65.8% |
| Avg cost / sample | – | $0.0006 |
| Avg speed / sample | – | 9.17s |
| By task | ||
| Object Detection | – | 52.1% $0.0011 |
| Counting | – | 47.3% $0.0003 |
| Identification | – | 90.6% $0.0003 |
| OCR | – | 88.1% $0.0007 |
| Data Extraction | – | 86.6% $0.0003 |
| Reasoning (low) | – | 29.8% $0.0003 |
| Reasoning (high) | – | 33.8% $0.0003 |
Qwen3.5 397B A17B vs Qwen3 VL 235B A22B Instruct: Overview
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.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.
Frequently Asked Questions
Qwen3.5 397B A17B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.