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LongCat 2.0
LongCat 2.0 API combines long-context reasoning, native tool use, and multi-step agentic workflows with prompt caching support.
LongCat 2.0 is Meituan's frontier agentic model with a 1M-token context window, up to 128K output tokens, native tool calling, and deep reasoning mode.
LongCat 2.0 is Meituan's trillion-parameter Mixture-of-Experts (MoE) model built for long-context agentic tasks. It features a 1M-token context window and supports up to 128K output tokens — making it one of the most capable models for document-heavy and multi-step workflows.
The model supports native tool calling, deep reasoning mode (thinking), and prompt caching for cost-efficient high-volume workloads.
API Pricing
Where to Use LongCat 2.0
Long-context document processing
Pass full contracts, research papers, or codebases in a single prompt. The 1M context window eliminates chunking and cross-reference issues.
Agentic workflows and tool calling
With native function calling and multi-step reasoning, LongCat 2.0 can plan, call tools, process results, and iterate without losing track of the original goal.
Complex reasoning tasks
Enable deep-thinking mode for STEM tasks, financial modeling, and logic problems. The model reasons through problems step-by-step before producing a final answer.
High-volume workloads with caching
Prompt caching at $0.0195/1M tokens makes it economical for repeated context patterns — batch pipelines, RAG systems, and multi-turn agents.
LongCat 2.0 is Meituan's trillion-parameter Mixture-of-Experts (MoE) model built for long-context agentic tasks. It features a 1M-token context window and supports up to 128K output tokens — making it one of the most capable models for document-heavy and multi-step workflows.
The model supports native tool calling, deep reasoning mode (thinking), and prompt caching for cost-efficient high-volume workloads.
API Pricing
Where to Use LongCat 2.0
Long-context document processing
Pass full contracts, research papers, or codebases in a single prompt. The 1M context window eliminates chunking and cross-reference issues.
Agentic workflows and tool calling
With native function calling and multi-step reasoning, LongCat 2.0 can plan, call tools, process results, and iterate without losing track of the original goal.
Complex reasoning tasks
Enable deep-thinking mode for STEM tasks, financial modeling, and logic problems. The model reasons through problems step-by-step before producing a final answer.
High-volume workloads with caching
Prompt caching at $0.0195/1M tokens makes it economical for repeated context patterns — batch pipelines, RAG systems, and multi-turn agents.
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