What Is a Mixture of Experts (MoE) Model?

Mixture of Experts (MoE) is a neural network architecture in which a model contains many specialized sub-networks ("experts") but activates only a few of them for each token it processes. A routing layer decides which experts handle which input, so an MoE model can hold hundreds of billions of parameters while spending only a fraction of that compute per answer.

How does a Mixture of Experts model work?

Instead of one dense stack of layers, an MoE model replaces certain feed-forward layers with a pool of experts plus a learned router. For every token, the router selects the top-k experts — typically two — and blends their outputs. Google's Switch Transformer (2021) demonstrated the approach at scale, and Mixtral 8x7B (December 2023) brought it to open weights: 46.7B total parameters with roughly 12.9B active per token.

Why MoE matters for AI search and GEO

MoE is one of the main reasons answer engines got dramatically cheaper to run between 2023 and 2026. DeepSeek-V3 activates about 37B of its 671B parameters per token, delivering frontier-adjacent quality at a fraction of dense-model inference cost. Cheaper inference changes engine behavior in ways marketers feel directly: engines can afford deeper query fan-out, longer synthesized answers, and more retrieval calls per user prompt — all of which expand the surface where brands get cited or ignored.

Dense vs. MoE at a glance

What marketers should take away

You do not optimize for MoE directly. But every architecture shift that lowers cost-per-answer increases the volume of AI answers being generated — and each engine upgrade can reshuffle which brands a model recalls or retrieves. Tracking visibility across model refreshes, rather than assuming stability, is the practical response; see the glossary entries on model refresh and answer volatility for the measurement side.