Mixture-of-Experts: Why Sparse Models Are Winning
If there is one architectural idea behind the cost curve bending downward in 2026, it is the mixture-of-experts, or MoE. The concept has been around for years, but it has moved to the centre of how leading models are built and served. Understanding it in plain terms explains a lot about why some very large models are also surprisingly cheap to run.
The core idea
A conventional "dense" model uses all of its parameters to process every token. If the model has a hundred billion parameters, every request pays for a hundred billion. A mixture-of-experts model is built differently. It contains many specialist sub-networks — the "experts" — and a lightweight router that, for each token, activates only a small handful of them. The model can therefore hold an enormous total parameter count while using only a fraction of it on any given request.
Why it cuts cost
The payoff is efficiency. Because only a few experts fire per token, the compute needed to serve a request is far lower than the model's total size would suggest. That means a sparse model can offer the knowledge capacity of a very large network at the serving cost of a much smaller one. In a market where price-per-token is a competitive battleground, that trade is enormously attractive, and it is a big part of why inference has grown cheaper even as models have grown more capable.
The trade-offs
MoE is not free lunch. Training these models is more complex: the router has to learn to send tokens to the right experts, and poor routing can leave some experts overused and others idle. Serving them demands a lot of memory, because all the experts must be available even though only a few are used at a time. There are also subtleties in keeping quality consistent, since the effective "model" handling your request depends on which experts the router chose. Labs have gotten much better at managing these issues, which is why MoE has moved from research curiosity to production mainstay.
Who uses it
Sparse architectures now appear across both proprietary and open-weight models. Mistral helped popularise strong open MoE models, DeepSeek has shipped notable sparse systems, and reporting suggests several of the largest proprietary models rely on mixture-of-experts designs as well, though labs are often reticent about internal architecture. The consistent theme is that when you want a lot of capacity without a proportional serving bill, MoE is the tool reached for.
For anyone trying to understand why 2026's models feel simultaneously bigger and cheaper, mixture-of-experts is much of the answer. It decouples a model's total capacity from the cost of using it — and that single decoupling has quietly reshaped the economics of the entire field.