I feel like this explains about a quarter to half of my token burn. It was never really clear to me whether tool calls in an agent session would keep the context hot or whether I would have to pay the entire context loading penalty after each call; from my perspective it's one request. I have Claude routinely do large numbers of sequential tool calls, or have long running processes with fairly large context windows. Ouch.
> The Anthropic prompt cache has a 5-minute TTL. Sleeping past 300 seconds means the next wake-up reads your full conversation context uncached — slower and more expensive. So the natural breakpoints:
> - *Under 5 minutes (60s–270s)*: cache stays warm. Right for active work — checking a build, polling for state that's about to change, watching a process you just started.
> - *5 minutes to 1 hour (300s–3600s)*: pay the cache miss. Right when there's no point checking sooner — waiting on something that takes minutes to change, or genuinely idle.
> *Don't pick 300s.* It's the worst-of-both: you pay the cache miss without amortizing it. If you're tempted to "wait 5 minutes," either drop to 270s (stay in cache) or commit to 1200s+ (one cache miss buys a much longer wait). Don't think in round-number minutes — think in cache windows.
> For idle ticks with no specific signal to watch, default to *1200s–1800s* (20–30 min). The loop checks back, you don't burn cache 12× per hour for nothing, and the user can always interrupt if they need you sooner.
> Think about what you're actually waiting for, not just "how long should I sleep." If you kicked off an 8-minute build, sleeping 60s burns the cache 8 times before it finishes — sleep ~270s twice instead.
> The runtime clamps to [60, 3600], so you don't need to clamp yourself.
Definitely not clear if you're only used to the subscription plan that every single interaction triggers a full context load. It's all one session session to most people. So long as they keep replying quickly, or queue up a long arc of work, then there's probably a expectation that you wouldn't incur that much context loading cost. But this suggests that's not at all true.
The part that does get cached - attention KVs - is significantly cheaper.
If you read documentation on this, they (and all other LLM providers) make this fairly clear.