Qwen 3.6 27B
A 27B dense multimodal model from Alibaba's Qwen team, optimized for agentic coding, reasoning, and long-context tasks.
Overview
What is Qwen 3.6 27B
Qwen 3.6 27B is a 27-billion-parameter dense language model from Alibaba's Qwen team, released in April 2026. It supports vision, reasoning, tool use, and web search, offers a 256K context window, and is optimized for agentic coding and repository-level reasoning.
Running it privately on Venice
On Venice, Qwen 3.6 27B runs with zero retention — your prompts are not stored or profiled. You get full access to its vision, reasoning, web search, and tool-use capabilities, plus structured JSON output and multi-image inputs, all under a private, permissionless inference tier with no Big-Tech surveillance.
Assessment
Strengths and limitations
- Agentic coding: strong at repository-level reasoning and frontend workflows.
- Multimodal: supports vision, video, multiple image inputs, and structured JSON output.
- Long context: 256K context window for large codebases and documents.
- Reasoning: built-in reasoning mode with thinking preservation across conversation history.
- Tool use: native function calling and web search integration for autonomous workflows.
- Efficient dense architecture: flagship-level performance in a 27B parameter footprint.
- Closed weights: not open-source, so it cannot be self-hosted or fine-tuned outside Venice.
- Output pricing: at $3.25 per 1M output tokens, it is more expensive than open-weight rivals such as DeepSeek V3.2.
- Dense model: lacks the active-parameter efficiency of MoE alternatives, so inference costs scale with the full 27B.
- Quantization: runs at FP8 on Venice, which may slightly reduce precision compared to higher-precision formats.
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
- Qwen (Alibaba Group)
- Released
- April 21, 2026
- Modality
- Text, vision, code
- Architecture
- Dense causal LM with vision encoder (Gated DeltaNet + Gated Attention hybrid)
- Parameters
- 27B
- Open weights
- No — proprietary
- Context window
- 256K tokens
- Max output
- 65.536K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Apr 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": "qwen3-6-27b",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.33 per 1M input tokens and $3.25 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 | Context window | Strongest at | Open weights | Price (Venice) |
|---|---|---|---|---|
| Qwen 3.6 27B | 256K tokens | Agentic coding & reasoning | No | $0.33 in · $3.25 out / 1M |
| DeepSeek V3.2 | 160K tokens | General reasoning & value | Yes | $0.33 in · $0.48 out / 1M |
| Google Gemma 4 31B Instruct | 256K tokens | Lightweight open inference | Yes | $0.12 in · $0.36 out / 1M |
| Kimi K2.6 | 256K tokens | Long-context open weights | Yes | $0.75 in · $3.50 out / 1M |
Dense flagship with vision, tool use, reasoning, and web search.
Use cases
What it is good for
- 01Agentic software development and repository-level coding assistants.
- 02Multimodal analysis of images, video, and documents with structured output.
- 03Long-context research and document Q&A over large codebases or reports.
- 04Iterative reasoning tasks where thinking preservation across turns matters.
- 05Automated workflows using tool use, web search, and function calling.
Prompting
Getting better results
Enable reasoning mode for complex coding or math problems to leverage thinking preservation.
Use structured JSON schema output for parsing logs, APIs, or tabular data.
For code generation, provide repository context in the first prompt to exploit the 256K window.
When using vision, upload multiple images in a single turn for comparative analysis.
Version history
Prior series.
Current dense release with vision and agentic coding.
FAQ
Frequently asked questions
Qwen 3.6 27B is a 27-billion-parameter dense language model from Alibaba's Qwen team, released in April 2026. It supports text, vision, reasoning, tool use, and web search, and is optimized for agentic coding and long-context workflows.
On Venice, Qwen 3.6 27B is billed at $0.33 per 1M input tokens and $3.25 per 1M output tokens. You pay only for what you use with no subscription required.
It is not free — usage is billed per token. It is also not open weights; the model is proprietary and cannot be downloaded or self-hosted.
Yes. It supports function calling, structured JSON output, web search, and vision inputs including multiple images and video.
Qwen 3.6 27B leads on agentic coding, vision, and reasoning with a 256K context window. DeepSeek V3.2 is fully open weights and significantly cheaper per output token, making it ideal for self-hosting and cost-sensitive scale.
Yes. Venice runs it under a zero-retention privacy tier — your prompts are not stored, profiled, or used for training.
Yes, the model accepts video input as well as multiple images, making it suitable for multimodal analysis and workflows.
The model supports up to 256K tokens of context on Venice, with a maximum output of 65,536 tokens per generation.
Run Qwen 3.6 27B privately
No prompt logging. No data used for training.