Ah, you're thinking about embeddings which are basically the encoder stack on a traditional transformer architecture. Modern GPT-like models (including Claude), however, drop the encoder and use decoder-only architectures.

I could imagine something where encoders pad up to the context length because causal masking doesn't apply and the self attention has learned to look across the whole context-window.