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Qwen3.5 397B A17B vs Qwen3 VL 30B A3B Instruct

Compare Qwen3.5 397B A17B and Qwen3 VL 30B A3B 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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Qwen3.5 397B A17B vs Qwen3 VL 30B A3B Instruct Comparison Table

Evals updated August 14, 2026Pricing updated August 15, 2026

PropertyQwen3.5 397B A17BQwen3 VL 30B A3B Instruct
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateFeb 2026Oct 2025
Context Window262K262K
Parameters397B31B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.390$0.130
Output $/1M$2.34$0.520
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 30B A3B 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 30B A3B Instruct

Qwen3 VL 30B A3B Instruct is an open-weight multimodal large language model developed by Alibaba as part of the Qwen family, built for instruction-following tasks that unify text generation with visual and video understanding. Released around October 2025 under the Apache-2.0 license, it targets efficient, high-fidelity vision-language reasoning across very long contexts.

The model accepts text and image inputs and produces text outputs, with strong performance in OCR, spatial reasoning, long-video understanding, and agentic or GUI-centric visual tasks. It uses a Mixture-of-Experts (A3B) design with ~31.1B total parameters and ~3B active per token, paired with Qwen3-VL’s unified multimodal stack (including Interleaved-MRoPE and DeepStack fusion) to process text, images, and video in a single architecture. OCR support expands to 32 languages, enhancing document workflows. With a native ~262K token context window (extendable further), it stands out today for its balance of scale, efficiency, long-context support, and open accessibility in multimodal systems.