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Qwen3.5 397B A17B vs Qwen3 VL 8B Instruct

Compare Qwen3.5 397B A17B and Qwen3 VL 8B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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QwenQwen3.5 397B A17B
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QwenQwen3 VL 8B Instruct
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Qwen3.5 397B A17B vs Qwen3 VL 8B Instruct Comparison Table

Evals updated July 24, 2026Pricing updated July 25, 2026

PropertyQwen3.5 397B A17BQwen3 VL 8B Instruct
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateFeb 2026Oct 2025
Context Window262K256K
Parameters397B8.8B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.390$0.117
Output $/1M$2.34$0.455
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Multi-Label Classification
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Qwen3.5 397B A17B vs Qwen3 VL 8B Instruct: Overview

Qwen3.5 397B A17B

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 8B Instruct

Qwen3 VL 8B Instruct is an open-weight multimodal vision-language model developed by Qwen / Alibaba Cloud as part of the Qwen3-VL series, designed for instruction-following tasks that combine text with visual inputs such as images and video. Released around October 2025 under the Apache-2.0 license, it targets developers who need capable multimodal reasoning without the scale or cost of very large models.

The model contains roughly 8.8 billion dense parameters and supports text, image, and video understanding with strong spatial perception, visual reasoning, and emerging visual agent abilities such as GUI interaction. A standout feature is its native ~256K token context window, extendable to around 1M tokens, enabling long-document reading and extended video comprehension. In today’s landscape, it balances openness, long-context capacity, and solid multimodal performance against heavier proprietary models. Typical applications include multimodal assistants, document and video analysis, visual question answering, and research or product prototyping where transparency and deployability matter.