Overview

Kimi k3 represents a significant leap in reasoning capabilities within the Kimi family, designed specifically for complex technical workflows. Boasting 2812B parameters with MXFP4 quantization, this thinking model delivers exceptional performance while maintaining efficiency. Developers gain access to a massive 1,048,576 token context window, enabling seamless analysis of extensive codebases or lengthy documentation without truncation. Integration is streamlined for rapid deployment, allowing API builders to execute their first call within minutes using standard endpoints and comprehensive documentation. The architecture supports advanced reasoning processes, making it ideal for researchers requiring rigorous benchmark performance on multilingual tasks including Arabic and English evaluations.

For enterprise adoption, Kimi k3 offers production-ready stability with native Arabic language support alongside English proficiency. This bilingual capability ensures accurate processing for diverse user bases without requiring separate pipelines. The model operates on a 2x credit multiplier relative to the base rate, reflecting its premium computational depth and high-fidelity outputs. Business decision makers can rely on transparent pricing structures and immediate availability on the starter tier. Whether scaling research pipelines or deploying customer-facing applications, this model provides the robustness needed for critical operations. Explore our comprehensive guides to compare benchmark data and integrate Kimi k3 into your existing infrastructure today without delay.

Specifications

Display Name kimi k3

Family Kimi-k3

Category Thinking

Parameters 2812B

Context Window 1,048,576 tokens

Quantization MXFP4

Min Tier Starter

Status Available

Pricing

2×

credits per 1,000 tokens

1K 2 Credits

10K 20 Credits

100K 200 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

Auditing large software repositories for security vulnerabilities

Synthesizing insights from extensive legal case files

Processing extended meeting transcripts for action items

Analyzing extensive scientific research papers for key findings

Summarizing entire book series for thematic connections

In-Depth Guide

Full Guide

Complete Guide to kimi k3 — LLM Resayil

kimi k3