Qwen: Qwen3.6 27B

Qwen3.6 27B is a dense 27-billion-parameter multimodal language model from Alibaba’s Qwen Team. Released in April 2026, it supports text, image, and video inputs, combines strong language and visual reasoning, and is optimized for agentic coding, repository-level reasoning, document understanding, and tool-use workflows.

Why It Looks Great

• Dense 27B architecture for practical deployment and predictable inference behavior
• Native multimodal support for text, image, and video understanding
• 262K-token context window for long prompts, large codebases, documents, and multi-turn workflows
• Up to 80K output tokens for extended reasoning, coding, and structured generation
• Strong agentic coding performance at a deployable open-weight scale
• Supports both thinking and non-thinking modes for flexible latency and reasoning trade-offs
• Function calling and tool-use support for agentic application workflows
• Structured output support for JSON responses and schema-constrained generation

Key Features

• Parameters: 27B
• Architecture: Dense
• Context Window: 262,144 tokens
• Max Input: 180,224 tokens
• Max Output: 81,920 tokens
• Input: Text, Image, Video
• Output: Text
• Vision: Supported
• Function Calling: Supported
• Structured Outputs: Supported
• Thinking Mode: Supported
• Audio Input: Not listed
• Image Generation: Not listed
• Supported Parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

Specifications

Pricing

How to Use

1. Write your prompt - describe the task, provide context, and specify the desired output format.
2. Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1
API Endpoint: chat/completions
Model ID: qwen/qwen3.6-27b

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.6-27b",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen/qwen3.6-27b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

• Model: qwen/qwen3.6-27b
• Provider: alibaba
• Best suited for agentic coding, repository-level reasoning, multimodal reasoning, document understanding, and structured output workflows