The model that powers a chat tool works on completely different principles from the one that generates an image or steers a car, even though we group them all under the same word. Those differences are not academic, because they decide what each model does well and what it costs to run. Understanding the main types and the ideas that separate them makes it far easier to judge which tool fits which job.
The models we use most
The everyday models behind most chat and writing tools are transformers, and what defines them is a mechanism called attention. Every token, which is roughly a word, carries a query for what it is looking for and a key for what it represents, along with a value that holds the information inside it. Each token can look at every other token and weigh what matters, which is what lets these models hold a thread of meaning across a long passage.
They build their output one token at a time, each following the last, which is what makes them autoregressive. The downside is that a mistake made early can be locked in, and the model carries it forward rather than correcting course.
The cost of scale
Letting every token attend to every other token is expensive, because the work grows with the square of the context. A context of around 1,000 tokens involves roughly a million interactions. Push that to 100,000 tokens and the figure climbs to about 10 billion. The vectors doing this work are large too, running to 128 or 256 dimensions in many models, so the cost mounts fast.
Context length is only one driver of that cost. The other is sheer model size, and newer designs attack each through a different route. State-space models such as Mamba aim to scale with context in a straight line rather than a curve, by holding a compressed sense of what matters so far rather than re-examining everything each time. A mixture of experts tackles size instead, using a coordinator inside the model that calls on only the relevant portion of it for each token rather than the whole thing. Some designs, such as Nemotron, combine both.
A different way to build
Diffusion models work on a completely different principle from the transformers behind most chat tools. Rather than producing one token after another, they start with pure noise and remove it step by step over a fixed number of passes until a result takes shape. They learn this by running the process in reverse during training, taking finished examples and adding noise until nothing is left, millions of times over.
The useful part is that a diffusion model can look at the whole output at once rather than committing to it left to right. That makes it less prone to the locked-in mistakes of an autoregressive model, since it can revise the whole picture as it goes. Diffusion has long been the engine behind image generation, and it is now showing real promise for language as well.
Models built from other models
Plenty of systems combine these approaches rather than picking one. Modern image generators such as Qwen and Sora are a good example. An autoregressive model first interprets the prompt and roughly sketches what the image should contain, then a diffusion model renders the detail. The sketch is what prevents the familiar failure of a hand with six fingers, because the structure is decided before the diffusion step fills it in.
From single models to systems
The clearest direction of travel is away from one model doing everything and towards several models working together. Some newer designs already unify many kinds of input and output in a single model, which matters in fields like robotics where a system has to read cameras and sensors and also drive motors.
The next step pairs a broad world model with narrower specialists. The world model holds a general understanding of how things behave, while each specialist handles a defined task and leans on that broader context when it needs to. These are separate models running alongside each other and sharing what they know, which is different from the single coordinated model behind a mixture of experts.
Matching the model to the job
Knowing which approach sits behind a tool tells you a lot about how it will behave and what it will cost to run. At ClearPoint, that understanding shapes how we match the right model to the right job, so the technology earns its place rather than being adopted for its own sake.
