The model is the important part, a huffman code or adaptive huffman or other sorts of encoders would be much better on a dataset based on the model. You need the model to also decode. And on a dataset of sufficient size, embedding the model and the benefit of it's memorization of the file can be offset.

A non-general compression algorithm (model - I don't mean a distinct llm, but "modeling data") targeted at a specific dataset will always do better than a general algorithm.

The reason I mentioned the "encoder" doesn't matter - arithmetic coding, for the data it is presented, will beat huffman/adaptive huffman every day, but it's the model that is where the real "compression" comes into play.

I've implemented enough "coders" over the years, including arithmetic for both commercial and research purposes (was a student of Glen Langdon).