Explain the lifecycle of test-time compute scaling on Sonnet 5's effort levels?

Test-time compute scaling refers to converting additional computational effort, spent at the moment a request is actually being processed, into better output quality, as opposed to improvements that come from changes made during the model's training process itself.

On Sonnet 5, this happens through the effort parameter's five levels, low through max, where each step up allows the adaptive thinking mechanism a larger ceiling to reason within, translating into deeper analysis, more thorough consideration of edge cases, or more careful tool-use planning at the cost of additional tokens and latency for that specific request.

This scaling isn't perfectly linear in either direction: very easy tasks may not benefit meaningfully from a higher effort ceiling, since the adaptive mechanism doesn't need to use all the headroom available to it, while very hard tasks can show diminishing returns at the very top of the scale, where reporting suggests max effort sometimes underperforms relative to its cost compared to simply switching to a more capable model tier.

Because this scaling happens per-request rather than being fixed at model-deployment time, it gives developers a live, request-by-request lever to trade cost and latency against output quality, which is the practical reason effort tuning, rather than picking a single fixed configuration for an entire application, is emphasized throughout Sonnet 5's prompting guidance.