LongCat-2.0.

LongCat-2.0

LongCat-2.0 is an open-source 1.6 trillion parameter Mixture-of-Experts (MoE) language model designed for frontier-scale AI capabilities. With approximately 48 billion activated parameters per token and native support for 1 million token contexts, it delivers exceptional performance on coding, agentic workflows, and long-horizon reasoning tasks—trained entirely on specialized AI ASIC hardware to demonstrate viable alternatives to traditional GPU infrastructure.

Product Highlights

• Massive Scale MoE Architecture: 1.6 trillion total parameters with efficient sparsity-driven activation, enabling unprecedented model capacity without proportional computational cost.
• LongCat Sparse Attention (LSA): Proprietary attention mechanism with streaming-aware indexing, cross-layer indexing, and hierarchical indexing—accelerating ultra-long context processing while maintaining quality.
• N-gram Embedding: Innovative 135-billion parameter embedding module that expands representation space 100× through token combinations, capturing richer local context than standard approaches.
• AI ASIC Native Training: First frontier-scale model trained entirely on custom accelerator superpods, proving stable, efficient large-scale training on non-GPU infrastructure.
• 1 Million Token Context Window: Native long-context support trained on hundreds of billions of tokens, enabling deep codebase understanding and extended document analysis.
• Multi-Expert Post-Training: Specialized Agent, Reasoning, and Interaction expert groups integrated through MOPD architecture for balanced capability across execution, logic, and human alignment.

Use Cases

• Enterprise Software Development: Repository-level code understanding, automated refactoring, SDK migration, and bug detection across million-line codebases.
• Autonomous AI Agents: Complex multi-step task execution with reliable tool use, API interaction, and self-correction for business process automation.
• Research & Analysis: Deep document processing, multi-hop reasoning over extensive corpora, and synthesis of information across ultra-long contexts.
• Technical Documentation: Accurate content generation from large technical specifications, maintaining factual consistency across extensive source materials.

Target Audience

LongCat-2.0 serves AI researchers, enterprise engineering teams, and developers building sophisticated agentic applications who require production-grade long-context capabilities and seek alternatives to proprietary model ecosystems—particularly organizations prioritizing infrastructure flexibility, open-source deployment, and cost-efficient scaling for complex reasoning and coding workloads.