Params: 1T
Kimi K2.5 is a native multimodal Mixture-of-Experts (MoE) large language model designed for advanced coding, vision reasoning, and autonomous agentic workflows. The model features a 1T parameter architecture with 32B activated parameters, 61 layers, 64 attention heads, and 384 experts (8 experts per token). It supports up to a 256K token context window and integrates a 400M-parameter MoonViT vision encoder for cross-modal understanding. The model employs native INT4 quantization with Quantization-Aware Training (QAT) to reduce inference latency and memory usage while maintaining strong performance.
Text GenerationInstruction Following+8
