Of course it's also been noted that this seems to be a new base model, so the change could certainly be in the model itself.
(URL is to diff since 2.1.98 which seems to be the version that preceded the first reference to Opus 4.7)
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.
No I am not joking. Every time you install something, there is a risk you clicked a wrong page with the absolute same design.
Every statement they make, hell even the models themselves are going to be doing this theater of "Ooooh scary uber h4xx0r AI, you can only beat it if you use our Super Giga Pro 40x Plan!!". In a month or two they'll move onto some other thing as they always do.