Moonshot AI Kimi K2

Kimi K2 Thinking is a reasoning-focused Mixture-of-Experts (MoE) large language model, designed as a step-by-step thinking agent capable of dynamically invoking tools during reasoning. The model features a 1T parameter architecture with 32B activated parameters, 61 transformer layers, 64 attention heads, and 384 experts (8 experts per token). It supports a 256K token context window and is optimized for deep reasoning and long-horizon autonomous workflows. The model employs native INT4 quantization with Quantization-Aware Training (QAT) to reduce inference latency and memory usage while maintaining strong performance.

Kimi K2 Instruct 0905 is an instruction-tuned Mixture-of-Experts (MoE) large language model developed, designed for advanced coding, agentic workflows, and long-horizon reasoning tasks. The model features a 1T parameter MoE architecture with 32B activated parameters, 61 transformer layers, 64 attention heads, and 384 experts (8 experts per token). It supports a 256K token context window and uses Multi-head Latent Attention (MLA) for efficient long-context processing.