qwen/qwen3.6-flash
Release date: 2026-04-27
1,000,000 context · $0.25/M input tokens · $1.50/M output tokens
Qwen3.6 Flash is a fast, efficient multimodal language model from Alibaba’s Qwen 3.6 series. It supports text, image, and video inputs with a 1M-token context window and up to 64K output tokens. The model is designed for high-throughput chat, lightweight agent workflows, long-document understanding, visual reasoning, summarization, extraction, and cost-sensitive production workloads. It supports thinking mode, function calling, built-in tools, structured outputs, and batch calling.
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our API:
import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen3.6-flash',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen3.6-flash',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Model Introduction
Qwen: Qwen3.6 Flash
Qwen3.6 Flash is a fast, efficient multimodal language model from Alibaba’s Qwen 3.6 series. It supports text, image, and video inputs with a 1M-token context window, making it a strong fit for high-volume chat, lightweight agents, long-document workflows, visual understanding, summarization, and structured extraction.
Why It Looks Great
- Fast, cost-efficient Qwen 3.6 model for high-throughput production workloads
- Multimodal text, image, and video input support for visual and document understanding
- 1M-token context window for long prompts, large files, and multi-turn workflows
- Up to 64K output tokens for extended answers and structured generation
- Thinking mode support for reasoning-heavy requests
- Function calling and built-in tool support for agentic workflows
- Structured output support for JSON responses and schema-constrained generation
- Batch calling support for large-scale offline or asynchronous workloads
Key Features
- Context Window: 1,000,000 tokens
- Max Input: 934,464 tokens
- Max Output: 65,536 tokens
- Input: Text, Image, Video
- Output: Text
- Vision: Supported
- Function Calling: Supported
- Built-in Tools: Supported
- Structured Outputs: Supported
- Batch Calling: Supported
- Thinking Budget: up to 128K tokens
- Supported Parameters: include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_p
Specifications
| Specification | Value |
|---|---|
| Provider | alibaba |
| Model Type | Chat Completions model |
| Architecture | text+image+video->text |
| Context Window | 1,000,000 tokens |
| Max Input | 934,464 tokens |
| Max Output | 65,536 tokens |
| Thinking Budget | 128K tokens |
| Input | Text, Image, Video |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
| Built-in Tools | Supported |
| Structured Outputs | Supported |
| Batch Calling | Supported |
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-flash
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-flash",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)Info
Supported Functionality
API Access Guide
Qwen3.6 Flash API
qwen/qwen3.6-flash
Qwen3.6 Flash is a fast, efficient multimodal language model from Alibaba’s Qwen 3.6 series. It supports text, image, and video inputs with a 1M-token context window and up to 64K output tokens. The model is designed for high-throughput chat, lightweight agent workflows, long-document understanding, visual reasoning, summarization, extraction, and cost-sensitive production workloads. It supports thinking mode, function calling, built-in tools, structured outputs, and batch calling.
Input
$0.25 /M
Output
$1.5 /M
Context
1000K
Max Output
66K
Vision
Supported
Tool Use
Supported
Try Qwen3.6 Flash on WaveSpeedAI
Access Qwen3.6 Flash through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Qwen3.6 Flash
How much does Qwen3.6 Flash cost via the API?+
Pricing on WaveSpeedAI: $0.25 per million input tokens and $1.50 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Qwen3.6 Flash?+
Qwen3.6 Flash supports up to 1000K tokens of context with up to 66K tokens of output per request.
Is Qwen3.6 Flash OpenAI-compatible?+
WaveSpeedAI exposes Qwen3.6 Flash at https://llm.wavespeed.ai/v1 through the OpenAI-compatible Chat Completions interface. Most OpenAI SDK clients work by changing the base URL and API key; optional fields depend on the selected model.
How do I get started with Qwen3.6 Flash?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with the model id shown above. Check the current model catalog for availability, capabilities, and pricing.