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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QwenQwen3.5 397B A17B
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QwenQwen3 VL 235B A22B Instruct
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Qwen3.5 397B A17B vs Qwen3 VL 235B A22B 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 235B A22B Instruct

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

PropertyQwen3.5 397B A17BQwen3 VL 235B A22B Instruct
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateFeb 2026Sep 2025
Context Window262K256K
Parameters397B235B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.385$0.200
Output $/1M$2.45$0.880
Vision Tasks
CaptioningDemoDemo
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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 Time56.61s
Median input tokensincl. image tokens1.1K
Median output tokens54
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 Time7.45s
Median input tokensincl. image tokens122
Median output tokens20
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