Definition
A mixture-of-experts (MoE) model is an AI built from many specialized sub-networks, with a router that switches on only the few needed for each request.
At a glance
- The model is split into many small “experts”; a router sends each request only to the few best-suited ones[1].
- This “sparse activation” lets a model hold huge knowledge while doing little work per request[3].
- The payoff: near-top-tier quality at much lower cost and faster responses.
- By 2026 nearly all frontier AI models use MoE.
How it works
A normal model runs its whole network for every request. An MoE model instead wakes only the relevant experts and leaves the rest idle[2]. Think of a large staff where only the two specialists who know the answer are pulled into the room.
Why it matters
Less of the model runs per request, so it stays cheap to operate. Mixtral 8x7B reaches 47B parameters but uses only ~13B per token, matching far larger models with much less compute[4]. For you, that means lower per-query cost and high-end quality without paying for a full model every time[5].
Bottom line
MoE gives you the knowledge of a giant AI at the running cost of a small one, which is why modern models keep getting smarter and cheaper at once.