Overview

Kimi K2.5 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 deep analytical workflows, code generation, and multi-step logical deduction. Developers integrating Kimi K2.5 through LLM Resayil gain access to a 128,000-token context window, enabling seamless processing of extensive documentation sets or large codebases without losing contextual fidelity. The FP16 quantization ensures high precision during inference, making it ideal for applications requiring nuanced understanding.

As a proprietary model within the Kimi family, K2.5 offers enterprise-grade stability and performance consistency. While the credit multiplier is set at 3.5x relative to the base rate, the enhanced reasoning capabilities justify the cost for demanding production environments. Available from the starter tier, this model allows teams to prototype sophisticated agents without high entry barriers. Choose Kimi K2.5 when your application demands superior chain-of-thought processing and robust handling of intricate data structures, ensuring reliable performance across diverse developer workflows.

For developers building autonomous agents or research tools, the thinking category designation confirms optimized latency for complex queries. LLM Resayil provides direct API access, streamlining deployment into existing pipelines. This model stands out for tasks where standard inference fails to capture necessary logical depth. It is the preferred choice for rigorous computational reasoning challenges.

Specifications

Display Name Kimi K2.5

Family Kimi

Category Vision

Parameters 1T MoE

Context Window 128,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 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-k2.5",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

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-k2.5",
            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-k2.5",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing long legal contracts for specific clauses

Solving complex mathematical reasoning problems step by step

Summarizing extensive research papers accurately and quickly

Debugging and optimizing large software codebases efficiently

Extracting key insights from multi-hour meeting transcripts

In-Depth Guide

Full Guide

Complete Guide to Kimi K2.5 — LLM Resayil

Kimi K2.5