A trained mathematical system that takes inputs and produces outputs based on patterns learned from data. In AI, "model" is the catch-all term for the thing you're actually using — whether it's GPT-4 generating text, Stable Diffusion generating images, or Whisper transcribing speech. A model is defined by its architecture (how it's structured), its parameters (what it learned), and its training data (what it learned from). When someone says "which model should I use?" they're asking about this.
Why it matters
Model is the single most used word in AI, and it means different things in different contexts. A "model" can refer to the architecture (Transformer), a specific trained instance (Claude Opus 4.6), a file on disk (a .gguf file), or an API endpoint. Understanding what a model actually is — and what it isn't — is the foundation for everything else.