Overview

Kimi K2 Thinking represents a significant advancement in reasoning-focused artificial intelligence, engineered specifically for complex problem-solving tasks. Built on a massive 1T MoE architecture, this model excels at decomposing intricate queries into manageable steps before generating responses. Developers integrating this variant gain access to an expansive 128,000-token context window, allowing for deep analysis of extensive codebases or lengthy documentation without losing coherence. The FP16 quantization ensures high precision during inference, making it ideal for applications requiring nuanced understanding and logical deduction. This architecture supports sustained attention over long sequences, ensuring critical details are never overlooked during execution.

Available through LLM Resayil, this proprietary model is designed for production environments where accuracy outweighs speed. The extended thinking process incurs a 4x credit multiplier relative to the base rate, reflecting the additional computational resources dedicated to deep reasoning. Accessible from the starter tier, it allows teams to prototype sophisticated agents without high entry barriers. When your application demands rigorous validation or multi-step planning, Kimi K2 Thinking provides the reliability needed to deploy confident AI-driven solutions. Seamless API integration ensures you can scale these capabilities within your existing infrastructure immediately.

Specifications

Display Name Kimi K2 Thinking

Family Kimi

Category Thinking

Parameters 1T MoE

Context Window 128,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

4×

credits per token

1K 4,000 Credits

10K 40,000 Credits

100K 400,000 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-k2-thinking",
    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-k2-thinking",
    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-k2-thinking",
            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-k2-thinking",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Complex logical reasoning and multi step problem solving

Deep analysis of lengthy technical documentation files

Advanced code debugging within large project contexts

Strategic planning for multi phase business operations effectively

Detailed scientific research paper comprehension and summary

In-Depth Guide

Full Guide

Complete Guide to Kimi K2 Thinking — LLM Resayil

Kimi K2 Thinking