Qwen3.6 27B | Qwen Multimodal LLM API Pricing

Name: Qwen3.6 27b API
Brand: qwen
Price: 0.54 USD
Availability: InStock

Utilisation de l'API

Utilisez les exemples de code suivants pour intégrer notre API :

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)

Introduction au modèle

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

Specification	Value
Provider	alibaba
Model Type	Chat Completions model
Architecture	Dense 27B multimodal model
Modalities	text+image+video->text
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
Release	April 2026

Pricing

Token Type	Cost
Input	$0.32 per million tokens
Output	$3.20 per million tokens

How to Use

Write your prompt - describe the task, provide context, and specify the desired output format.
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

Qwen3.6 27b API

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 with a 262K-token context window and up to 80K output tokens. Designed for agentic coding, repository-level reasoning, multimodal reasoning, document understanding, and tool-use workflows, it supports both thinking and non-thinking modes while remaining practical to deploy at a widely used 27B dense-model scale.

Questions fréquentes sur Qwen3.6 27b

Combien coûte l'API Qwen3.6 27b ?+

Tarification sur WaveSpeedAI : $0.54 par million de tokens d'entrée et $3.24 par million de tokens de sortie. Le prompt caching et le traitement par batch sont facturés séparément et réduisent le coût effectif sur les charges longues et répétitives.

Quelle est la fenêtre de contexte de Qwen3.6 27b ?+

Qwen3.6 27b prend en charge jusqu'à 262K tokens de contexte et jusqu'à 82K tokens de sortie par requête.

Qwen3.6 27b est-il compatible avec OpenAI ?+

Oui. WaveSpeedAI expose Qwen3.6 27b via un endpoint compatible OpenAI à https://llm.wavespeed.ai/v1. Pointez le SDK officiel d'OpenAI vers cette base URL avec votre clé API WaveSpeedAI — aucune autre modification de code requise.

Comment démarrer avec Qwen3.6 27b ?+

Connectez-vous à WaveSpeedAI, créez une clé API dans Access Keys, puis envoyez une requête à https://llm.wavespeed.ai/v1/chat/completions avec l'id du modèle affiché ci-dessus. Les nouveaux comptes reçoivent des crédits gratuits pour évaluer Qwen3.6 27b.

Tarification

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