Say I want to ask a model about a code base 500k tokens in size. The questions would not be to generate code, but rather to help understand it in a way that require the model to globally reason about everything at once.

How might performance compare between:

1.) Using a model like Gemini to load it all into context at once

2.) Using one of the various summarization systems/embedding/RAG etc.

3.) Fine tuning the whole code base into Gpt-4o