Parameters
Definition
The internal settings a model adjusts as it learns, and the rough measure of its size. Modern models have billions of them. Loosely, you can think of parameters as the dials the system tunes during training to get better at its task. More parameters can mean a more capable model, though it isn't a simple case of bigger being better, and it comes at a cost in computing power.
Why It Matters
When you see a model described as having "70 billion parameters," this is what that means. It's a size label, useful for comparison but not the whole story.
What does that look like in practice?
A model billed as '70 billion parameters' has that many internal dials it tuned while learning.
What people actually mean when they say this
When someone boasts about 'billions of parameters,' they mean the model is large, which can help, but isn't the whole story of how good it is.
Last reviewed: June 2026
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