What Is Mixture of Experts (MoE)?
Mixture of Experts (MoE) is a neural-network design that splits a model into many specialized sub-networks ("experts") and activates only the few most relevant ones for each input. This gives the capacity of a very large model while keeping the compute cost of a much smaller one.
How MoE works
A routing component looks at each input and selects a small subset of experts to handle it. Because only a fraction of the network runs per token, MoE models can be enormous in total size yet efficient to run.
MoE vs multi-model AI
MoE routes within a single model to internal experts. Multi-model AI routes across separate, independent models (like GPT and Claude) and combines them. They are complementary: one is architecture, the other is orchestration. Allecta operates at the multi-model level.
See it in action
Allecta applies mixture of experts (moe) directly: it queries several leading AI models in parallel and synthesizes one cross-verified answer with consensus scoring — so you get the benefit of this concept without building anything.
Try Allecta free →Mixture of Experts (MoE): FAQ
Which models use Mixture of Experts?
Several modern open and closed models use MoE designs, including various Mistral and DeepSeek models, to balance capability and cost.
Is MoE the same as an AI ensemble?
No. MoE selects internal experts within one model; an ensemble combines multiple complete models. Both aim for better results, at different levels.