Overview

Kimi k2.7 code stands as a powerhouse within the Kimi-k2 family, engineered for complex reasoning and extensive context handling. With 1042B parameters quantized to INT4 and a massive 262,144 token context window, this thinking model excels at processing large codebases and technical documentation without losing coherence. Developers can integrate it immediately via our standard API endpoints, ensuring your first call completes within minutes. The architecture supports nuanced bilingual tasks, delivering state-of-the-art performance on both Arabic and English benchmarks compared to similar tier alternatives. This satisfies rigorous research pipeline requirements while providing the stability needed for production deployments.

Production readiness is central to this release, offering robust Arabic language support alongside English for regional enterprise applications. Operating on a starter tier with a 2x credit multiplier relative to the base rate, it provides a cost-effective solution for high-volume inference needs. Pricing transparency is maintained through our dashboard, displaying costs in major regional currencies including KWD, SAR, and AED without requiring sales consultation. This model is optimized for demanding workflows where accuracy and context retention are critical, making it a reliable choice for scaling intelligent applications across diverse linguistic environments.

Specifications

Display Name kimi k2.7 code

Family Kimi-k2

Category Vision

Parameters 1042B

Context Window 262,144 tokens

Quantization INT4

Min Tier Starter

Status Available

Pricing

2×

credits per token

1K 2,000 Credits

10K 20,000 Credits

100K 200,000 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-k2.7-code",
    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-k2.7-code",
            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-k2.7-code",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Refactoring large legacy codebases with extensive context

Debugging complex software architecture across multiple files

Generating comprehensive technical documentation from full projects

Analyzing extensive log files for root cause

Planning system architecture for large scale applications

In-Depth Guide

Full Guide

Complete Guide to kimi k2.7 code — LLM Resayil

kimi k2.7 code