Model

A model is what training produces: a fixed set of parameters, often billions of them, stored in a file you load and run. Those parameters encode the patterns the model learned from its training data. Nothing about the model changes when you use it; you feed in text and it computes an output. All the "intelligence" is baked into those frozen numbers.

Parameters, not rules

The parameters are weights, learned automatically during training, not rules a human wrote. That is why you cannot open a model and find the line that decides how it writes a for-loop. The behaviour is distributed across the whole network. It also explains why models are hard to fully predict: you are working with learned statistics, not a program someone specified.

Why "which model" matters

Models differ in ways that directly affect your work:

• Size and capability. Larger, more capable models tend to reason better over long, messy code, at higher cost and latency.
• Training and recency. Two models trained differently will have different strengths and different knowledge cutoffs.
• Specialisation. Some models are tuned for chat, some for code, some for tool use.

When a coding tool lets you pick a model, you are trading off quality, speed, and cost. For a quick rename, a small fast model is fine. For untangling a subtle bug across ten files, reach for the strongest one you have.