MoE (Mixture of Experts)
A model architecture where the network contains multiple specialized sub-networks (experts), and a routing mechanism activates only a subset of experts for each input. A 400B-parameter MoE model might activate only 50B parameters per token, achieving near-frontier quality at the inference cost of a much smaller model. MoE architectures (used in models like Mixtral and reportedly in GPT-4) are why some AI agents can deliver high-quality responses at surprisingly low latency and cost—the model is large in total but efficient per-query.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Frequently asked questions
- Why does MoE matter for AI agent builders?
- MoE models offer a better quality-to-cost ratio than dense models of the same total parameter count. For agent builders, this means access to near-frontier reasoning at mid-tier pricing—or self-hosting a high-quality model on hardware that couldn't run the equivalent dense model. When evaluating models for your agent, check whether they use MoE architecture—it affects cost, latency, and hosting requirements.