Mixture of Experts (MoE)
A model architecture where multiple specialized sub-networks ('experts') exist within a single model, and a routing mechanism activates only the most relevant experts for each input. MoE models can be very large in total parameters but fast and efficient at inference because only a fraction of the network is active per request. This architecture powers several frontier models and enables better performance without proportional increases in compute cost.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Example
A model with 8 expert sub-networks routes a coding question to its code-specialized experts and a marketing question to its language/creative experts—using the same model but different internal pathways for each task.
Frequently asked questions
- How does MoE affect AI agent costs?
- MoE models are often cheaper per token than dense models of equivalent quality because they activate fewer parameters per request. For agents processing thousands of requests daily, this translates directly to lower operating costs without sacrificing output quality.