Bigger context windows don't mean what people think

Was learning a little about agent context windows and how they work. Based on interrogating Claude Chat, the following information was verified against the official documentation on context. Context window ≠ cache. Jumping to a 1M-token window just gives you a bigger buffer. It doesn't mean earlier content gets cached automatically — prompt caching is a separate, opt-in feature. The 1M window isn't only for the newest models.Compaction works the same way at the larger limit. Performance tracks absolute tokens, not percentage. This is my own rule of thumb, not something the docs state: a model tends to feel the weight of a long context by raw token count, not by how full the window is. So a nearly-full 200K session can behave worse than a 250K session inside a 1M window — raw tokens are what dilute attention. The docs only go as far as "compact when context starts affecting performance," so treat the specific numbers as judgement, not fact. Set auto-compact by token count — there's no…