A model is, physically, a giant list of parameters: numbers, often billions of them, that were tuned during training. People also call them weights. They are not settings you configure. They are values the training process discovered on its own, and together they encode every pattern the model picked up, from grammar to how a React component is usually structured.
Size is a parameter count
When you hear "a 70-billion-parameter model," that number is the parameter count. More parameters give a model more capacity to store patterns, which usually means stronger reasoning over messy code, at the cost of more compute, higher latency, and a bigger bill. It is a rough proxy for capability, not a guarantee: how a model was trained matters just as much as how many parameters it has.
Frozen at inference
The key thing for daily work is that parameters are set once and then frozen. During inference, when you actually use the model, nothing about them changes. Your conversation does not nudge a single weight. That is why a coding agent cannot "learn" your codebase by chatting, and why everything it knows about your specific project has to arrive as context on each request.
Related terms
Model
A model is the trained artifact at the centre of every AI coding tool: a large file of numbers (parameters) that, given some text, produces the most likely continuation. When people say "which model are you using," this is the thing they mean.
Read definition →Training
Training is the process that produces a model: showing it enormous amounts of text and adjusting its parameters until it gets good at predicting what comes next. It happens once, before you ever use the model.
Read definition →Inference
Inference is the act of running a trained model to get an answer: text goes in, a prediction comes out. Every message you send to a coding agent is an inference. It is the opposite end of the lifecycle from training.
Read definition →