KAT Coder is KwaiKAT's most advanced agentic coding model in the KAT-Coder series.
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.
