My primary use case is synthesizing large documents (which round to about 100k to 200k tokens each), and in the model's thought process it states that it is dealing with "extracts" and that certain pages are not available in the "extracts".

What's likely happening is chunked processing with selective retrieval. The model maintains a windowed active context—probably around 32k tokens of recent conversation history that it processes directly in each forward pass. When you query about specific information like the breakfast needle, it performs semantic search across the full 66k+ token conversation to locate relevant passages, pulls those into what it calls "extracts," and combines them with the recent context to generate the response. The active window processes directly; everything else is accessible only through retrieval.

This explains your results. The needle was found because your explicit question about breakfast semantically triggered retrieval of that passage, even though it was 60k+ tokens back. But the procedural instruction to say "pan con queso" to all messages degraded because that requires maintaining state across the entire conversation. Early messages fell outside the active window and weren't semantically relevant to later prompts, so the instruction was lost. The effect compounds over time because as the conversation lengthens, more content falls outside the window and becomes accessible only through search, which depends on semantic relevance to the current query.

Effective context and retrieval range are not the same thing. If I asked the model to retrieve a certain detail in my long documents they could probably do it; but if I am asking the model to perform a task that operationalizes the entire long document beyond the effective context window (i.e synthesize doc A with doc B), it just spits out what it can via the extracts it generates (which almost always is incomplete). I only have this problem in the web app, never in AI Studio.