KAT Coder Pro is KwaiKAT's most advanced agentic coding model in the KAT-Coder series. Designed specifically for agentic coding tasks, it excels in real-world software engineering scenarios, achieving 73.4% solve rate on the SWE-Bench Verified benchmark.
Code Example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("ATLASCLOUD_API_KEY"),
base_url="https://api.atlascloud.ai/v1"
)
response = client.chat.completions.create(
model="kwaipilot/kat-coder-pro-v2",
messages=[
{
"role": "user",
"content": "hello"
}
],
max_tokens=1024,
temperature=0.7
)
print(response.choices[0].message.content)Install
Install the required package for your language.
pip install requestsAuthentication
All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Headers
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.
Submit a request
import requests
url = "https://api.atlascloud.ai/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"messages": [{"role": "user", "content": "Hello"}],
"max_tokens": 1024
}
response = requests.post(url, headers=headers, json=data)
print(response.json())Input Schema
The following parameters are accepted in the request body.
Example Request Body
{
"model": "kwaipilot/kat-coder-pro-v2",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}Output Schema
The API returns a ChatCompletion-compatible response.
Example Response
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1700000000,
"model": "model-name",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
}
}Atlas Cloud Skills
Atlas Cloud Skills integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.
Supported Clients
Install
npx skills add AtlasCloudAI/atlas-cloud-skillsSetup API Key
Get your API key from the Atlas Cloud dashboard and set it as an environment variable.
export ATLASCLOUD_API_KEY="your-api-key-here"Capabilities
Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.
MCP Server
Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.
Supported Clients
Install
npx -y atlascloud-mcpConfiguration
Add the following configuration to your IDE's MCP settings file.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
KAT-Coder: Elite AI Code Generation
73.4% SWE-Bench Verified - Surpassing Industry Leaders
KAT-Coder is the flagship closed-source AI coding model by Kwaipilot (Kuaishou's AI research division), representing the pinnacle of agentic code generation technology. Powered by a Mixture-of-Experts architecture with 72B active parameters and trained through large-scale agentic reinforcement learning, KAT-Coder achieves a 73.4% score on SWE-Bench Verified, ranking among the best code generation models globally alongside GPT-5 High and Claude Sonnet 4.5.
Industry-Leading Performance
KAT-Coder competes with the world's best code generation models on SWE-Bench Verified, the industry standard benchmark for real-world software engineering tasks
Core Capabilities
Mixture-of-Experts Architecture
Leverages advanced MoE design with 72B active parameters out of over 1 trillion total, delivering state-of-the-art performance on complex software engineering tasks.
- 72B active parameters for optimal efficiency
- Built on Qwen model family foundation
- Optimized for enterprise-scale codebases
Agentic Reinforcement Learning
Trained through multi-stage pipeline including large-scale agentic RL, enabling autonomous completion of complex software engineering tasks.
- Shared prefix trajectory optimization
- Entropy shaping advantage mechanism
- Training on real Git commits and PRs
Multi-Tool Integration
Built-in capabilities for interacting with thousands of tools through real sandbox execution data, enabling practical software development workflows.
- Interaction data from thousands of tools
- Real execution in sandbox environments
- Seamless API and CLI integration
256K Context Window
Extensive context support enables handling sophisticated multi-turn coding interactions and managing large-scale codebases effectively.
- Handle multiple files simultaneously
- Maintain long conversational history
- Cross-file reasoning and refactoring
Git-Native Training
Trained on real Git commit and PR data from enterprise repositories, understanding version control workflows natively.
- Real repository commit patterns
- Pull request best practices
- Code review and collaboration patterns
Enterprise-Grade Quality
High-quality domain-specific data including instruction following across 30+ categories and general reasoning capabilities.
- 30+ instruction following categories
- Advanced reasoning for edge cases
- Production-ready code generation
Multi-Stage Training Pipeline
KAT-Coder's training methodology represents a significant advancement in AI coding models, combining multiple training stages for optimal performance
Mid-Training
Foundation stage with coding knowledge injection and high-quality domain-specific data
Supervised Fine-Tuning (SFT)
Instruction following and dialogue training across 30+ categories
Reinforcement Fine-Tuning (RFT)
Enhanced reasoning and problem-solving capabilities
Agentic Reinforcement Learning
Large-scale RL on enterprise codebases with autonomous task completion
Perfect For
Complex Debugging
Identify and fix bugs across large codebases with multi-file context understanding
Large Codebase Refactoring
Systematic refactoring with awareness of architectural patterns and dependencies
Multi-File Code Generation
Generate coherent code across multiple files with proper integration
Repository Understanding
Analyze and comprehend large repositories with deep architectural insights
Technical Specifications
Open-Source Variants
While KAT-Coder Pro is closed-source, Kwaipilot has released open-source alternatives available on HuggingFace under Apache-2.0 license
KAT-Dev-32B
Open SourceOptimized 32B parameter variant with multi-stage training including supervised fine-tuning and reinforcement learning.
KAT-Dev-72B-Exp
ExperimentalExperimental 72B parameter variant pushing the boundaries of open-source code generation capabilities.
Experience Elite Code Generation
Start using KAT-Coder today through our API. Join developers worldwide who trust KAT-Coder for mission-critical software engineering tasks.
