LongCat-2.0

LongCat-2.0 is a vast 1.6T-parameter Mixture-of-Experts (MoE) model engineered for extensive context processing and specialized tasks. Key capabilities include: * 1 million token context window * Sparse attention mechanism for efficiency * Specialized post-training for code generation * Tailored for agentic task execution * ~48 billion active parameters for focused processing This powerful model was developed using advanced computing infrastructure, ensuring robust performance. Its unique LongCat Sparse Attention mechanism allows it to handle unusually long contexts with remarkable efficiency, making it suitable for complex problem-solving scenarios that demand deep contextual understanding. LongCat-2.0 is particularly adept at code-related applications due to its targeted post-training, producing high-quality code and assisting developers in intricate programming challenges. Furthermore, its design caters to agentic workflows, enabling systems to perform multi-step operations and decision-making processes. It is an ideal foundation for developers and engineers building sophisticated applications that require extensive context comprehension, precise code output, or advanced task delegation. Integrations with existing developer platforms like Claude Code and OpenClaw further enhance its utility in a technical ecosystem.