New Model Support

We now support the Qwen3-Coder-480B-A35B model (model identifier: Qwen3-Coder), which is optimized for code generation and understanding, featuring a 256k context length and 64k maximum output length. It supports tools like Claude Code, Cline, Roo Code and more.

Quick Start

iFlow API provides 100% OpenAI-compatible interface services, allowing you to seamlessly switch to our AI services for higher performance and more cost-effective solutions.

Step 1: Get Your API Key

1. Visit iFlow and complete registration and login
2. Generate your exclusive API KEY by clicking Personal Information menu on the user settings page
3. Safely store the API KEY for subsequent API calls

💡 Tip: The API KEY has full account permissions. Do not disclose it to others.

Step 2: Understand Supported Models

We provide a variety of high-performance AI models for you to choose from. To get the latest model information, detailed parameter configurations, and usage instructions, please visit our models page:

View Complete Model List

Click to view all supported models

On the models page, you can:

• View detailed information about all available models
• Learn about context length and output limits for each model
• Check model performance characteristics and applicable scenarios
• Copy model ID for API calls

Step 3: Configure API Parameters

Use the following configuration information to call the iFlow API:

Parameter Name	Parameter Value	Description
HTTP URL	https://apis.iflow.cn/v1/chat/completions	Chat interface, supports streaming and non streaming
API Key	your key	Obtain from Console, click Personal Information menu on the user settings page
OpenAi Base URL	https://apis.iflow.cn/v1	OpenAI SDK usage

Step 4: Start Calling the API

Basic Examples

Here are examples of calling the iFlow API using different programming languages:

OpenAI-Python

from openai import OpenAI

client = OpenAI(
  base_url="https://apis.iflow.cn/v1",
  api_key="<YOUR_IFLOW_API_KEY>",
)

completion = client.chat.completions.create(
  extra_body={},
  model="TBStars2-200B-A13B",
  messages=[
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
)
print(completion.choices[0].message.content)

OpenAI-TypeScript

import OpenAI from 'openai';
const openai = new OpenAI({
  baseURL: "https://apis.iflow.cn/v1",
  apiKey: "<YOUR_IFLOW_API_KEY>"
});
async function main() {
  const completion = await openai.chat.completions.create({
    model: "TBStars2-200B-A13B",
    messages: [
      {
        "role": "user",
        "content": "What is the meaning of life?"
      }
    ],
    
  });

  console.log(completion.choices[0].message);
}

main();

Bash/cURL

curl https://apis.iflow.cn/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "TBStars2-200B-A13B",
    "messages": [
      {"role": "system", "content": "You are a professional AI assistant."},
      {"role": "user", "content": "Please introduce the history of artificial intelligence development"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
  }'

Python

import requests

# Configure API
url = "https://apis.iflow.cn/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

# Request data
data = {
    "model": "TBStars2-200B-A13B", 
    "messages": [
        {"role": "user", "content": "Explain the basic principles of quantum computing"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
}

# Make request
response = requests.post(url, json=data, headers=headers)
result = response.json()

# Print result
print(result["choices"][0]["message"]["content"])

JavaScript

const response = await fetch('https://apis.iflow.cn/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'TBStars2-200B-A13B',
    messages: [
      {role: 'user', content: 'Write a Python implementation of the quicksort algorithm'}
    ],
    temperature: 0.7,
    max_tokens: 1000
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

Next Steps

• 📖 Detailed Documentation: View the complete API Reference Manual
• 🔧 Advanced Configuration: Learn more about parameter configuration options
• 💬 Technical Support: Contact Us for help

🚀 Start Building: Now that you've mastered the basics, you can start integrating the iFlow API into your projects!