Complete guide to using Kimi K3 API on TokenPAPA. Setup steps, Python code examples, streaming, pricing 10% below official, and comparison with official Moonshot AI API.

Kimi K3 API Guide: How to Access Moonshot's 2.8T Open-Weight Model

Kimi K3 is Moonshot AI's flagship 2.8-trillion-parameter model. This guide covers everything you need to integrate it through TokenPAPA's API.

Prerequisites

A TokenPAPA account
Your API key from the dashboard
OpenAI Python library (or any OpenAI-compatible SDK)

pip install openai

Basic Chat Completion

from openai import OpenAI

client = OpenAI(
    base_url="https://tokenpapa.ai/v1",
    api_key="your-tokenpapa-key"
)

completion = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant powered by Kimi K3."},
        {"role": "user", "content": "Explain the Mixture of Experts architecture in simple terms."}
    ],
    temperature=0.7,
    max_tokens=2048
)

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

curl https://tokenpapa.ai/v1/chat/completions \
  -H "Authorization: Bearer *** \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What makes Kimi K3 special?"}
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://tokenpapa.ai/v1',
  apiKey: 'your-tokenpapa-key',
});

const completion = await client.chat.completions.create({
  model: 'kimi-k3',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is Kimi K3?' },
  ],
});

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

Streaming (Real-Time)

from openai import OpenAI

client = OpenAI(
    base_url="https://tokenpapa.ai/v1",
    api_key="your-tokenpapa-key"
)

stream = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Write a short poem about artificial intelligence."}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Multi-Turn Conversations

from openai import OpenAI

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-tokenpapa-key")

messages = [
    {"role": "system", "content": "You are a knowledgeable coding assistant."},
    {"role": "user", "content": "Write a Python function to calculate fibonacci numbers."},
]

# First response
resp1 = client.chat.completions.create(model="kimi-k3", messages=messages)
answer = resp1.choices[0].message.content
print("Kimi K3:", answer[:100] + "...")

# Follow-up
messages.append({"role": "assistant", "content": answer})
messages.append({"role": "user", "content": "Now optimize it for performance using memoization."})

resp2 = client.chat.completions.create(model="kimi-k3", messages=messages)
print("Optimized:", resp2.choices[0].message.content[:100] + "...")

Long Document Analysis

Kimi K3 excels at long-context tasks. Here's how to analyze a large document:

# Load a long document
with open("research_paper.txt", "r") as f:
    document = f.read()

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-tokenpapa-key")

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {"role": "system", "content": "Summarize the following research paper in 3 bullet points."},
        {"role": "user", "content": document}
    ],
    max_tokens=1024
)

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

Bilingual (Chinese-English) Mode

Kimi K3's strongest capability is seamless Chinese-English switching:

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-tokenpapa-key")

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {"role": "user", "content": "Translate this to Chinese and explain: 'Attention is all you need' is a seminal paper in deep learning."}
    ]
)
print(response.choices[0].message.content)

Pricing

Channel	Price (Input/1M tokens)	Price (Output/1M tokens)
TokenPAPA	10% below official ✅	10% below official ✅
Moonshot AI (Official)	Higher	Higher

Kimi K3 via TokenPAPA costs 10% less than going direct to Moonshot AI. Plus, you get unified access to 30+ other models under one API key and one billing dashboard.

Error Handling

from openai import OpenAI, APIError, RateLimitError, APITimeoutError

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-tokenpapa-key")

try:
    response = client.chat.completions.create(
        model="kimi-k3",
        messages=[{"role": "user", "content": "Hello!"}],
        timeout=30
    )
    print(response.choices[0].message.content)
    
except RateLimitError:
    print("Rate limited. Retrying with backoff...")
except APITimeoutError:
    print("Request timed out. The model may be under heavy load.")
except APIError as e:
    print(f"API error: {e.status_code} - {e.message}")

Model Switching Pattern

One of the biggest advantages of TokenPAPA is the ability to compare Kimi K3 with other flagship models without changing your code:

models = {
    "kimi-k3": "Moonshot flagship (2.8T, open-weight)",
    "gpt-5.5": "OpenAI flagship",
    "claude-opus-4": "Anthropic flagship",
    "deepseek-v4-pro": "DeepSeek flagship",
}

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-key")

for model_id, description in models.items():
    resp = client.chat.completions.create(
        model=model_id,
        messages=[{"role": "user", "content": "Solve: 25 * 47 + 13 * 29"}],
        max_tokens=256
    )
    print(f"\n{model_id} ({description}):")
    print(resp.choices[0].message.content)

Next Steps

Sign up at tokenpapa.ai — free account, $2 free credits
Try Kimi K3 — the 2.8T open-weight flagship at 10% below official pricing
Explore other models — switch between 30+ models from one API

Kimi K3 API Guide: How to Access Moonshot's 2.8T Open-Weight Model (2026)