MoE or Mixture of Experts is the architecture powering the most capable AI models today, including GPT-5, Gemini, and Llama 4. Here is what it actually is and why it matters.
The Problem with Dense Models
Traditional neural networks activate all of their parameters for every single input. If a model has 70 billion parameters, all 70 billion are doing work for every token generated. This is incredibly computationally expensive.
How MoE Works
MoE models split the network into multiple expert sub-networks. For any given input, only a small subset of these experts are activated — typically 2 to 8 out of potentially hundreds. A router network learns to direct each input to the most relevant experts.
A 700 billion parameter MoE model might only activate 70 billion parameters per query — giving you the capability of a massive model at the computational cost of a much smaller one.
Real-World MoE Models
GPT-5 is widely believed to use a MoE architecture with hundreds of experts. Mistral Mixtral 8x7B was one of the first open-source MoE models to achieve widespread adoption. Google Gemini models also use MoE at their core.