Explain the internal difference between Claude Sonnet 5's effort parameter and its adaptive thinking mechanism?

Adaptive thinking is the mechanism that determines whether and how much the model reasons on a per-request basis, dynamically responding to what it perceives about the specific task's difficulty rather than following a fixed, pre-configured amount every time.

The effort parameter functions as a ceiling or scaling factor applied to that adaptive process - it doesn't force a fixed amount of reasoning on every request regardless of difficulty, but it bounds how far the model's adaptive judgment is allowed to go, so the same task run at different effort levels can produce meaningfully different reasoning depth even though the adaptive mechanism itself is making the moment-to-moment decisions within that boundary.

This two-layer design is why effort doesn't behave like a simple linear cost dial: a very easy request at high effort may still reason only lightly, since the adaptive layer judged that little reasoning was actually needed, while a genuinely hard request at a lower effort level may still reason more than a simpler request would at that same level, within whatever ceiling that effort setting allows.

Practically, this means tuning effort is about setting the right ceiling for a given workload's typical task difficulty, while trusting adaptive thinking to handle the actual per-request variation within that ceiling, rather than treating effort as a direct, request-by-request override of reasoning depth.

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