Model ID
meituan-longcat/LongCat-Flash-Chat
Capabilities
LongCat Flash Chat
LongCat Flash Chat is a 562B-parameter Mixture-of-Experts (MoE) language model optimized for agentic tasks and high-throughput inference. It dynamically activates 18.6-31.3B parameters per token using zero-computation experts and a Shortcut-connected MoE (ScMoE) design. Built on a 28-layer transformer with 64 attention heads and 6,144 hidden size, it supports up to 128K token context. The model employs a multi-stage training pipeline with reasoning-focused pretraining, agentic post-training, and multi-agent task synthesis, enabling advanced reasoning, coding, and iterative interaction capabilities.
About this model
LongCat Flash Chat is a 562B-parameter Mixture-of-Experts (MoE) language model optimized for agentic tasks and high-throughput inference. It dynamically activates 18.6-31.3B parameters per token using zero-computation experts and a Shortcut-connected MoE (ScMoE) design. Built on a 28-layer transformer with 64 attention heads and 6,144 hidden size, it supports up to 128K token context. The model employs a multi-stage training pipeline with reasoning-focused pretraining, agentic post-training, and multi-agent task synthesis, enabling advanced reasoning, coding, and iterative interaction capabilities.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meituan-longcat/LongCat-Flash-Chat). Released under MIT. Further reading: Paper
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · SGLang. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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