Conversational AI
Build fast chatbots and virtual assistants that respond in real time.
Qwen3.5-9B — Fast Dense Model for Everyday Tasks
Qwen3.5-9B is a fast 9-billion parameter model for everyday tasks — quick answers, drafting, simple coding, and casual conversation. Try it free in your browser.
Ready To Chat
Online
Qwen3.5-9B is ready
Qwen3.5-9B is the default model for this page. Fast everyday Qwen 3.5 model for drafting, QA, and lightweight coding.
Pick a model, decide whether this needs web search or thinking, then start with a real prompt.
Text
Lightweight
Starter prompts
Free to try in the browser. The model card includes single-device serving examples if you want to run it yourself.
Parameters
9B
Architecture
Dense
Context
262K native
License
Apache 2.0
Jump to another Qwen model
Overview
Where Qwen3.5-9B Fits in the Qwen 3.5 Family
Qwen3.5-9B is the smallest public dense release in the Qwen 3.5 line. It gives you the low-latency path through the family, especially when the job is drafting, lightweight coding, or short factual work rather than long, messy reasoning.
Small Dense Baseline
This is the simplest open Qwen3.5 dense checkpoint to deploy and compare against larger dense or MoE options.
Single-Device Friendly
This 9B checkpoint is friendly to single-device setups, though VRAM still depends on precision, framework, and context length.
262K Native Context
Qwen3.5-9B supports 262,144 native tokens and can stretch further with the right serving stack.
Qwen3.5-9B vs GPT-OSS-120B & Qwen3.5-4B
The compact 9B model — it matches or beats a 120B model on knowledge and reasoning.
Model
MMLU-Pro
GPQA Diamond
LiveCodeBench v6
Qwen3.5-9BThis model
MMLU-Pro
82.5
GPQA Diamond
81.7
LiveCodeBench v6
65.6
GPT-OSS-120B
MMLU-Pro
80.8
GPQA Diamond
80.1
LiveCodeBench v6
—
Qwen3.5-4B
MMLU-Pro
79.1
GPQA Diamond
76.2
LiveCodeBench v6
55.8
Scores are from official release pages and model cards; blanks (—) are not reported by the source.
Source: Qwen3.5 official release & Hugging Face model cardsUse Cases
What Qwen3.5-9B Is Best For
Qwen3.5-9B excels at tasks where speed matters more than maximum depth.
Content Drafting
Generate blog posts, emails, summaries, and marketing copy quickly.
Code Suggestions
Get quick code completions, simple refactors, and boilerplate generation.
Q&A and Search
Answer factual questions and extract information from documents.
Local Deployment
Run on your own hardware via Ollama or vLLM with minimal setup.
Rapid Prototyping
Iterate fast on prompts and workflows before scaling to larger models.
FAQ
Qwen3.5-9B FAQ
Common questions about using Qwen3.5-9B.
How does Qwen3.5-9B compare to Qwen3.5-27B?
Qwen3.5-9B is faster and uses less memory, but Qwen3.5-27B delivers stronger reasoning and better performance on complex tasks. Choose 9B for speed, 27B for depth.
Can I run Qwen3.5-9B on my local machine?
Yes. The model card includes single-device serving examples for Qwen3.5-9B. Exact hardware needs still depend on precision, framework, and how much context you keep enabled.
What is the context length of Qwen3.5-9B?
Qwen3.5-9B supports 262,144 native tokens and can reach roughly 1.01M tokens in compatible serving stacks.
Is Qwen3.5-9B good for coding?
It handles simple coding tasks well — completions, boilerplate, basic refactors. For complex multi-file reasoning or debugging, Qwen3.5-Plus or the larger MoE models perform better.
How much VRAM does Qwen3.5-9B need?
At Q4 quantization, around 5-6 GB. Full precision (BF16) requires about 18 GB. The exact number depends on your framework and context length.
Is Qwen3.5-9B good for RAG pipelines?
Yes. Its fast inference speed and small footprint make it a solid choice for retrieval-augmented generation where latency matters more than maximum reasoning depth.
Can Qwen3.5-9B handle multilingual tasks?
Yes. Qwen 3.5 models support 100+ languages, including strong CJK coverage. The 9B size handles everyday multilingual tasks well.
Does Qwen3.5-9B support tool calling?
Yes. All Qwen 3.5 models support function calling. The 9B size is fine for lightweight tool flows, while larger models are better for long multi-step chains.