The OpenCode devs talk about this on Twitter a lot, e.g. https://xcancel.com/thdxr/status/2048268697790300343

> tool call pruning breaks cache and people will tell you this is horrible and expensive

> except i looked at some anthropic data and real user behavior ends up with better cache hits and 30% less spend

> even this is needs to be analyzed further, it's just not simple

> for openai data it's inverted! cache hit ratio is actually better [sic: I think he meant worse based on the screenshot] with tool call pruning turned on

> but the net $ saved is only 5%

> kimi is a funny one - it has better cache hits with pruning on...but is also more expensive!

There was also another thread recently where he discussed that pruning improves user experience (models are smarter with less context) but I can't find it.

This can also be disabled in the config: https://opencode.ai/docs/config/#compaction

My understanding of caching with most models/providers is that a prefix substring of the context has to be reused for a cache hit, but not necessarily the whole entire context window. So if you prune tool calls from the history, you're going to get one cache miss on the newly-pruned history, and then you're going to be getting cache hits on every subsequent turn, with a lower number of input tokens. If you prune subsequent tool calls after that, you would still get a cache hit for the already-pruned portion of the context, just not the full context.

They are. Empirical evidence on my side. Because attention is sparse across the context. It's not truly treating a million token the way it treats a fraction of that count. For performance.