Qwen3.5 9b vs Qwen3 VL 235B A22B Instruct

Compare Qwen3.5 9b 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 9b
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Qwen3.5 9b vs Qwen3 VL 235B A22B Instruct: Overview

Qwen3.5 9b

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 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 9b vs Qwen3 VL 235B A22B Instruct Comparison Table

PropertyQwen3.5 9bQwen3 VL 235B A22B Instruct
OrganizationQwenQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateMar 2026Sep 2025
Context Window262K256K
Parameters9B235B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.100$0.200
Output $/1M$0.150$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%
Overall Score
71.64%
Avg Response Time8.99s
Defect Detection
86.7%(13/15)
Document Understanding
66.7%(6/9)
Object Counting
30%(3/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
84.2%(16/19)