Parameters
Also: model weights
The internal settings inside an AI model that define what it knows and how it responds — set during training and fixed afterward. When you hear "70 billion parameters," that just means the model has 70 billion tunable numerical values it learned during training. More parameters generally means more capacity, but it's not the only factor in quality. You don't control these — they're baked into the model before you ever use it.
In practice
When Anthropic says Claude has billions of parameters, they mean the model has billions of learned numerical values baked in from training — the "knowledge" encoded in the model's weights. You can't see or change them. More parameters generally means more capacity, but the relationship between parameter count and quality isn't straightforward.
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