Overview

Kimi K3 delivers unprecedented reasoning power with 2812B parameters optimized via MXFP4 quantization. Designed for complex problem-solving, this thinking model supports a massive 1,048,576 token context window, allowing developers to process entire codebases or extensive documentation in a single pass. Integration is streamlined through our standard API endpoints, enabling API builders to execute their first call within minutes using familiar authentication flows. The architecture ensures high throughput while maintaining precision across multi-step logical tasks, making it ideal for agents requiring deep analysis without latency bottlenecks.

Researchers will find robust performance across Arabic and English tasks, with significant improvements in nuanced language understanding compared to previous iterations. While specific benchmark tables are available in our documentation, Kimi K3 excels in scenarios demanding strict adherence to complex instructions. For enterprise decision makers, this model operates on an 18x credit multiplier relative to base rates, reflecting its premium capability and production readiness. Access is gated at the enterprise tier, ensuring dedicated support and stability for mission-critical applications. This balance of raw computational power and structured access makes Kimi K3 a definitive choice for scaling advanced AI workflows securely.

Specifications

Display Name Kimi K3

Family Kimi-k3

Category Thinking

Parameters 2812B

Context Window 1,048,576 tokens

Quantization MXFP4

License OTHER

Min Tier Enterprise

Status Available

Pricing

18×

credits per token

1K 18,000 Credits

10K 180,000 Credits

100K 1,800,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

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

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

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="kimi-k3",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "kimi-k3",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

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

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire legal codebases for compliance checks regularly

Summarizing very long meeting transcripts into actionable specific items

Debugging large legacy software repositories efficiently and effectively

Conducting deep research across thousands of academic papers

Processing long financial reports for risk assessment goals

In-Depth Guide

Full Guide

Complete Guide to Kimi K3 — LLM Resayil

Kimi K3