Qwen 3.5 9B
Alibaba's 9B open-weight multimodal model with hybrid attention, 256K context, and native tool use.
Overview
What is Qwen 3.5 9B
Qwen 3.5 9B is Alibaba's open-weight, multimodal language model released in March 2026. Built on a 9-billion-parameter hybrid architecture mixing linear and full attention, it handles vision and text, offers a 256K-token context window, and supports tool use, reasoning, and web search for agents and coding.
Running it privately on Venice
On Venice, Qwen 3.5 9B runs under a zero-retention privacy tier — your prompts are not stored or used for training. You get full access to its vision, reasoning, function calling, and web search capabilities with open-weight sovereignty, while Venice handles inference in fp8 without Big-Tech surveillance.
Assessment
Strengths and limitations
- Open-weight Apache 2.0 model you can self-host or run privately on Venice without vendor lock-in.
- Native multimodal design with early fusion handles vision and text in one model, supporting multiple image inputs and structured JSON output.
- Long 256K context window and 32K max output suit large-scale RAG, document analysis, and coding workflows.
- Built-in tool use, reasoning, and web search make it a strong agent backbone.
- Efficient hybrid attention architecture (Gated DeltaNet) delivers high throughput with lower latency than traditional full-attention transformers.
- Extensive multilingual coverage (201 languages) for global deployment.
- At 9B parameters it lacks the raw depth of larger frontier models (e.g., 30B+ or 100B+ dense models) on the hardest reasoning tasks.
- Benchmark scores are impressive for its size, but real-world performance varies by task and prompt design.
- Vision capabilities are unified but may not match dedicated, larger vision-language models in fine-grained visual reasoning.
- Not uncensored on Venice; safety alignment is present.
- 256K context is large but still shorter than some rivals offering 500K or 1M tokens.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Alibaba
- Released
- March 2, 2026
- Architecture
- Decoder-only, 3:1 hybrid linear/full attention (Gated DeltaNet)
- Parameters
- 9B
- Modality
- Text and image input (multimodal)
- Open weights
- Yes — Apache 2.0
- Context window
- 256K tokens
- Max output
- 32.768K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Mar 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-5-9b",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.10 per 1M input tokens and $0.15 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Best for | Context window | Open weights | Price (Venice) |
|---|---|---|---|---|
| Qwen 3.5 9B | The lightweight, open-weights choice with native vision, reasoning, and the lowest per-token cost. | 256K tokens | Yes | $0.10 in · $0.15 out / 1M |
| DeepSeek V3.2 | Larger MoE open model with strong reasoning; 3x the input cost and a shorter 160K context. | 160K tokens | Yes | $0.33 in · $0.48 out / 1M |
| Google Gemma 4 31B Instruct | Bigger 31B open model with native audio/video support; 2.4x higher output price. | 256K tokens | Yes | $0.12 in · $0.36 out / 1M |
| Kimi K2.6 | Open-weight with strong long-context performance, but 7.5x more expensive on input. | 256K tokens | Yes | $0.75 in · $3.50 out / 1M |
The lightweight, open-weights choice with native vision, reasoning, and the lowest per-token cost.
Use cases
What it is good for
- 01Coding assistants and IDE copilots that need long context for repositories.
- 02Multimodal agents combining tool use, web search, and vision to automate workflows.
- 03Local or private self-hosting on consumer hardware with open weights.
- 04Multilingual customer support and content generation across 201 languages.
- 05Long-document RAG and summarization with 256K context.
Prompting
Getting better results
Feed long codebases or documents directly into the context to exploit the 256K window.
Use JSON schema / structured output for reliable agent parsing and tool chaining.
Pair images with explicit text instructions for best vision-language results.
For reasoning tasks, prompt step-by-step explicitly to leverage its reasoning mode.
Version history
Current release — 9B dense hybrid-attention model with native vision and tool use.
FAQ
Frequently asked questions
Qwen 3.5 9B is Alibaba's open-weight, multimodal language model released in March 2026. It features a 9B-parameter hybrid decoder architecture, supports vision and text input, and offers tool use, reasoning, and web search capabilities.
Venice bills per token at $0.10 per 1M input tokens and $0.15 per 1M output tokens. There is no subscription required; you pay only for what you use.
Yes. The model weights are released under the Apache 2.0 license, allowing self-hosting, fine-tuning, and commercial use without restrictions.
Yes. On Venice it supports tool use, function calling, structured JSON output, and web search, making it suitable for building agents and automated workflows.
Yes. It is a natively multimodal model that accepts image input alongside text, including multiple image inputs in a single conversation.
Qwen 3.5 9B is leaner, cheaper, and faster for text-centric agents, while Gemma 4 31B offers more parameters and native audio/video support. Choose Qwen for cost-efficiency and Gemma for richer media modalities.
It runs under Venice's private zero-retention tier. Your prompts are not stored, profiled, or used for training, and no personal account history is built from your generations.
The model supports a 256K-token context window with a maximum output of 32,768 tokens, enabling long-document analysis and large codebases.
Yes. Because it is a 9B-parameter open-weight model, it can be downloaded and self-hosted on consumer hardware or run privately through Venice's API.
Run Qwen 3.5 9B privately
No prompt logging. No data used for training.