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1. Log in to the Platform

Create a SiliconFlow account.

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2. View Model List and Details

Visit the Models page to check the details of currently available models, including model pricing, maximum usage limits for users, and more. You can also test the models directly through the “Online Experience” feature on the model details page.

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3. Experience GenAI Capabilities in the Playground

Go to the “Playground” page. On the left sidebar, you can choose language models, text-to-image models, or image-to-image models. Select the corresponding model to start real-time testing. Enter the relevant parameters and prompt, then click the “Run” button to see the results generated by the model.

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4. Use SiliconFlow API to Access GenAI Capabilities

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4.1 Create an API Key

Go to the API Keys page, click “Create API Key,” and generate your API key.

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4.2 Make Service Calls via REST API

You can directly use your API key for online calls on the platform’s “Documentation” page, where you can generate code snippets in the corresponding programming language.

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4.3 Call via OpenAI Interface

Some large language models currently support being called via the OpenAI library.
After installing Python 3.7.1 or a higher version and setting up a virtual environment, you can install the OpenAI Python library. Run the following command from your terminal/command line:

pip install --upgrade openai  

Once completed, running pip list will display the Python libraries installed in your current environment. Confirm that the OpenAI Python library has been successfully installed.
You can then call the API using OpenAI’s related interfaces. The platform currently supports most OpenAI-related parameters.

from openai import OpenAI  

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.ap.siliconflow.com/v1")  
response = client.chat.completions.create(  
    model='deepseek-ai/DeepSeek-R1',  
    messages=[  
        {'role': 'user',  
        'content': "tell me a story"}  
    ],  
    stream=True  
)  

for chunk in response:
    if not chunk.choices:
        continue
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
    if chunk.choices[0].delta.reasoning_content:
        print(chunk.choices[0].delta.reasoning_content, end="", flush=True)