Qwen3.5 397B A17B vs Qwen3.5 9b
Compare Qwen3.5 397B A17B and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
Compare Qwen3.5 397B A17B vs Qwen3.5 9b 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
Qwen3.5 397B A17B vs Qwen3.5 9b: 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.
Qwen3.5-9B 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.
Qwen3.5 397B A17B vs Qwen3.5 9b Comparison Table
| Property | Qwen3.5 397B A17B | Qwen3.5 9b |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Mar 2026 |
| Context Window | 262K | 262K |
| Parameters | 397B | 9B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.385 | $0.100 |
| Output $/1M | $2.45 | $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% | ||
| Overall Score | 58.21% | 71.64% |
| Avg Response Time | 56.61s | 8.99s |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 54 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Defect Detection | 66.7%(10/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 66.7%(6/9) |
| Object Counting | 20%(2/10) | 30%(3/10) |
| Object Understanding | 64.3%(9/14) | 71.4%(10/14) |
| Spatial Understanding | 57.9%(11/19) | 84.2%(16/19) |
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