Mixture of Experts

What it is

Mixture of Experts (MoE) is an approach to building large artificial intelligence models. Instead of having one set of neurons (the basic computing units) handle every problem, the model is divided into separate groups called experts. Each expert specializes in a certain kind of task or type of input. When the model gets a question, a routing system decides which experts are best suited to help answer it, and only those experts do the heavy lifting.

This matters because it lets companies make models with very large total parameter counts while still keeping each individual response fast and efficient. A parameter is a number the model adjusts during training to learn patterns. More parameters usually mean more ability to learn, but they also cost more to run.

Who is behind it

The main companies mentioned here are Mistral AI, JetBrains, and Reflection AI.

Mistral AI released a 1.05-trillion parameter model in October 2026. JetBrains released a 12-billion-parameter coding model called Mellum2.1 in the same month. Reflection AI launched a 501-billion-parameter open-weight model called Beam, aimed at coding agents.

These models all fall on one end of a spectrum that runs from small focused models to enormous ones that can handle many different tasks. The trend is toward bigger parameter counts and broader capability. The 1.05 trillion parameter Mistral model has a context window of one million tokens — a token is a chunk of text the model reads or writes — and was tested on cybersecurity performance.

Where it stands now

The field is moving quickly toward models with hundreds of billions or trillions of parameters. Open-weight models — models whose internal numbers are available for others to study and build on — are becoming more common alongside proprietary ones. Coding is a major focus area, with several new models built specifically for software development and agentic tasks, where an AI takes on a multi-step goal and plans its own steps to reach it.

No single version of MoE has become the clear standard yet. Different teams are trying different sizes and approaches, and the results are still being compared.

Questions people ask

What does Mixture of Experts mean in simple terms?

It is a way of building a big AI model by splitting it into smaller specialist groups. Only the relevant specialists are used for each question, so the model stays fast even though it is very large.

Why do companies keep making bigger models?

Larger models with more parameters tend to handle more tasks better and can work with longer inputs. Architecture designs like Mixture of Experts help make those large models usable without making every answer extremely slow.

What is an open-weight model?

It is a model whose internal trained numbers are shared publicly. Other researchers and developers can study them, fine-tune them for their own use, or build new tools on top of them.