using open models and local AI via llamacpp, I'm compacting (via agent directed) between 55k-85k; going up to 120k is rare unless the agent is actually following a non trivial log tracing or some other thing that absolutely requires keeping large context.

I've also tailored an agent system prompt with llamacpp reasoning budget and message that asks the agent to spawn subagents then compress to reduce context bloat. It's in opencode using the dynamic context pruning, so it's mostly keeping a vector&direction without having to bloat up.

It mostly works to allow iteration into multiple sub components.