Kimi K2 is an open-source agentic MoE language model that activates 32 billion parameters out of a 1 trillion-parameter capacity to execute tasks rather than only reason. The architecture uses 384 experts and selects eight per token to minimize computation, reduce memory footprint, and improve concurrency. Pretraining used the MuonClip optimizer across 15.5T tokens with no reported instability. Kimi K2 focuses on writing code, executing tasks, and completing agentic workflows. DeepSeek R1 contrasts by emphasizing deep reasoning through reinforcement learning and extended-context thinking rather than built-in execution-focused design.
Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters developed by China's Moonshot AI: Unlike reasoning models that can afford to "think slowly," agents need to act much faster and efficiently. By activating only 32 billion out of 1 trillion parameters (about 3.2%), Kimi K2 can: Respond faster - Less computation per token means quicker responses
What exactly makes Kimi K2 effective at execution? The model uses 384 distinct experts, with eight being selected to process each token, allowing for highly efficient computation. More importantly, it was trained with the MuonClip optimizer, achieving pre-training of a 1T parameter MoE model on 15.5T tokens with zero training instability. DeepSeek R1, conversely, demonstrates that the reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT.
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