In machine learning, a model is the artifact produced by training: an architecture (such as a transformer) combined with the specific weight values learned from a dataset and training objective. "Model" is used loosely to mean the architecture, the trained weights, or the full deployed system (weights plus serving code and configuration) depending on context — a distinction worth noting when comparing claims about a model's size, capability, or cost.