How to Use Kimi K3 API in Python

What is Kimi K3?

Kimi K3 is a flagship AI model developed by Moonshot AI. It is designed for long-horizon coding, knowledge work and reasoning, with native multimodal capabilities and a context window of up to 1 million tokens.

For developers, the interesting part is that Kimi K3 is available through an API. This means you can use the model inside your own Python applications instead of interacting with it only through a chat interface.

Kimi's API also follows the OpenAI API format, which makes it easier for developers who already know the OpenAI Python SDK to get started.

In this guide, we will connect a Python application to Kimi K3 through an OpenAI-compatible API endpoint and send our first request.

What you need before starting

You need:

Python installed on your computer.
An API key.
Access to Kimi K3.
The OpenAI Python SDK.

If you are using Velona, you can access supported AI models through its API platform and use the OpenAI-compatible endpoint in your Python application.

Create a Python project

Start by creating a project directory:

mkdir kimi-k3-python
cd kimi-k3-python

It is a good idea to use a virtual environment:

python -m venv venv

Activate it on Linux or macOS:

source venv/bin/activate

On Windows:

venv\Scripts\activate

Install the OpenAI Python SDK

Install the SDK with pip:

pip install openai

The reason we can use the OpenAI SDK is that Kimi K3 supports an OpenAI-compatible API format.

This means the basic application structure is familiar if you have already worked with OpenAI-compatible APIs.

Store your API key securely

Your API key should never be treated as ordinary application data.

Do not write it directly into your source code:

api_key = "your-secret-key"

Instead, use an environment variable.

Create a .env file:

API_KEY=your_api_key_here

Then install python-dotenv:

pip install python-dotenv

Add the environment file to .gitignore:

.env

This reduces the risk of accidentally committing your API key to a public repository.

Connect Python to Kimi K3

Create a file called main.py.

If you are using Velona's OpenAI-compatible endpoint, the client can be configured like this:

import os

from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://velona.in/v1"
)

The API key authenticates your request.

The base_url tells the SDK which API endpoint should receive the request.

Send your first Kimi K3 request

Now you can make a chat completion request.

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {
            "role": "user",
            "content": "Explain what an API is in simple language."
        }
    ]
)

print(response.choices[0].message.content)

The model ID for Kimi K3 is kimi-k3.

If the request is configured correctly, the generated response will be available in the returned object.

The basic flow is:

Python application
       |
       v
OpenAI Python SDK
       |
       v
Velona API
       |
       v
Kimi K3
       |
       v
Generated response
       |
       v
Python application

Understanding the request

There are two important parts of the request you should understand.

The first is the model:

model="kimi-k3"

This tells the API which model should process the request.

The second is the messages array:

messages=[
    {
        "role": "user",
        "content": "Explain what an API is in simple language."
    }
]

This contains the conversation sent to the model.

You can also provide system instructions:

messages=[
    {
        "role": "system",
        "content": "You are a helpful Python programming assistant."
    },
    {
        "role": "user",
        "content": "Explain Python decorators."
    }
]

This allows you to control the role and behavior you want from the model.

Why Kimi K3 is interesting for long-context applications

One of the notable capabilities of Kimi K3 is its context window of up to 1 million tokens.

A large context window can be useful when an application needs to work with substantial amounts of information in a single task.

For example, an application could work with:

Large technical documents.
Long codebases.
Research material.
Large conversation histories.
Multiple related documents.

Instead of treating every piece of information as a completely separate task, an application can provide a much larger amount of relevant context to the model.

However, a large context window does not mean you should send unnecessary information with every request.

Only send the context your application actually needs.

Use Kimi K3 for coding tasks

Kimi K3 is designed for long-horizon coding and knowledge work, which makes it useful for developer-focused applications.

For example, you can ask it to analyze a Python function:

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {
            "role": "user",
            "content": """
Review this Python function and identify potential problems:

def calculate_total(items):
    total = 0
    for item in items:
        total += item["price"]
    return total

Explain your reasoning and suggest improvements.
"""
        }
    ]
)

print(response.choices[0].message.content)

The same pattern can be used for debugging, code explanation, documentation and other programming tasks.

Kimi K3 uses thinking by default

Kimi K3 is designed as a reasoning model and its API documentation states that thinking is enabled by default.

The API also provides a reasoning_effort parameter with supported levels such as low, high and max.

For example:

response = client.chat.completions.create(
    model="kimi-k3",
    reasoning_effort="high",
    messages=[
        {
            "role": "user",
            "content": "Analyze this Python algorithm and suggest an optimization."
        }
    ]
)

The exact parameters available through a particular API endpoint should always be checked against its current documentation.

Turn Kimi K3 into a reusable Python function

Instead of repeating the API request throughout your application, create a reusable function:

def ask_kimi(prompt):
    response = client.chat.completions.create(
        model="kimi-k3",
        messages=[
            {
                "role": "user",
                "content": prompt
            }
        ]
    )

    return response.choices[0].message.content

You can now use it anywhere in your application:

answer = ask_kimi(
    "Give me three ideas for a Python automation project."
)

print(answer)

This simple abstraction becomes useful when your application starts having multiple AI-powered features.

Add error handling

API requests can fail for many reasons.

Your API key could be invalid, the model ID could be incorrect, the request could contain an unsupported parameter, or the service could temporarily reject the request.

Use a try and except block:

def ask_kimi(prompt):
    try:
        response = client.chat.completions.create(
            model="kimi-k3",
            messages=[
                {
                    "role": "user",
                    "content": prompt
                }
            ]
        )

        return response.choices[0].message.content

    except Exception as error:
        print("Kimi API request failed:")
        print(error)

During development, the error message can help you identify which part of the request needs attention.

Start with the smallest request

If your Kimi K3 integration is not working, do not immediately test it with your entire application.

Start with something extremely simple:

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {
            "role": "user",
            "content": "Say hello."
        }
    ]
)

print(response.choices[0].message.content)

If this works, add your real prompt, application logic and additional parameters one step at a time.

This makes debugging significantly easier.

Keep your API key on the backend

If you are building a website or web application, do not place your API key in frontend JavaScript.

A safer architecture is:

Browser
   |
   v
Your backend
   |
   | API key
   v
AI API
   |
   v
Kimi K3
   |
   v
Your backend
   |
   v
Browser

The browser sends the user's request to your server. Your server then makes the authenticated API request.

This keeps your API credentials away from the browser.

Understand Kimi K3 API usage

AI API usage is generally measured according to the amount of processing your requests consume.

For text requests, token usage is an important part of that calculation.

Input
  |
  v
Input tokens
  |
  v
Kimi K3
  |
  v
Output tokens
  |
  v
Usage cost

A short prompt and short answer will generally consume less than a request containing a large document, long conversation history and detailed generated output.

If you are using an INR-based API wallet, monitoring usage helps you understand how quickly your balance is being consumed.

Common Kimi K3 API problems

Invalid API key

Check that your environment variable contains the correct key.

print(
    "API key loaded"
    if os.getenv("API_KEY")
    else "API key missing"
)

Never print the actual API key.

Incorrect model ID

Use the exact current model identifier:

kimi-k3

Do not assume that older Kimi model names will work with Kimi K3.

Incorrect endpoint

Check that your base_url matches the API provider you are using.

Unsupported parameters

Parameters can differ between model versions. If a request fails after you add an optional parameter, remove it and test the basic request again.

Unexpected API usage

If your application is consuming more balance than expected, check for repeated loops, retries, large prompts and unnecessarily long conversation histories.

Build a simple Kimi K3 application

Once the basic function works, you can turn it into a simple command-line assistant:

while True:
    prompt = input("You: ")

    if prompt.lower() == "exit":
        break

    answer = ask_kimi(prompt)

    print("Kimi K3:", answer)

Now you have a small application that sends user prompts to Kimi K3.

You: Explain recursion
Kimi K3: Recursion is...

You: Give me a Python example
Kimi K3: Here's an example...

You: exit

The same ask_kimi() function can later be connected to a web application, chatbot, API backend or AI agent.

What can you build with Kimi K3?

Once Kimi K3 is connected to your application, you can build features such as:

AI coding assistants.
Document analysis tools.
Long-context chatbots.
Research assistants.
Programming tutors.
Content generation tools.
Knowledge-base assistants.
AI agents.

The API turns the model from something you interact with manually into a capability that your software can call whenever it needs it.

Final thoughts

Using Kimi K3 from Python is straightforward when you break the process into a few steps:

Create API key
      |
      v
Install OpenAI SDK
      |
      v
Configure API endpoint
      |
      v
Use model: kimi-k3
      |
      v
Send prompt
      |
      v
Receive response
      |
      v
Build application logic

Kimi K3 is particularly interesting for applications that need long-context processing, coding, knowledge work and reasoning. Its API compatibility also means developers familiar with the OpenAI SDK can get started without learning an entirely different programming interface.

For Indian developers using Velona, the same API-first workflow can be used to access supported models while managing usage through an INR-based setup.

Start with one simple request. Confirm that authentication, the endpoint and model are working. Then gradually add longer prompts, application logic and more advanced capabilities.

That is the easiest way to go from testing Kimi K3 to actually building with it.

How to Use Kimi K3 API in Python: Complete Developer Guide