Qwen qwen3.5-flash-02-23

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.

Why It Looks Great

• Large Language Model architecture for efficient processing
• 1000000 context window for long document handling
• Competitive pricing at $0.1/$0.4 per million tokens

Key Features

• Context Window: 1000000 tokens
• Max Output: 65536 tokens
• Vision: Supported
• Function Calling: Supported

Specifications

Pricing

How to Use

1. Write your prompt — describe the task, provide context, and specify 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/qwen3.5-flash-02-23

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/qwen3.5-flash-02-23",
    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/qwen3.5-flash-02-23",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

• Model: qwen/qwen3.5-flash-02-23
• Provider: Qwen