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: 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.
Qwen3.5 397B A17B vs Qwen3 VL 235B A22B Instruct Comparison Table
| 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 | $0.385 | $0.200 |
| Output $/1M | $2.45 | $0.880 |
| 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 | 58.21% | |
| Avg Response Time | 56.61s | |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 54 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Defect Detection | 66.7%(10/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 64.3%(9/14) | |
| Spatial Understanding | 57.9%(11/19) | |
| OCR | ||
| Overall Score | 68.56% | |
| Avg Response Time | 7.45s | |
| Median input tokensincl. image tokens | 122 | |
| Median output tokens | 20 | |
| Est. cost / taskon this benchmark | $0.0001 | |
| Focused Scene OCR | 57.6%(57/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 100%(30/30) | |
| Text Recognition | 70%(21/30) | |
| VQA & Extraction | 68.3%(41/60) | |
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