Explain the execution flow of a Claude Sonnet 5 request that omits the thinking field?

The request reaches Sonnet 5 without any explicit thinking configuration, and because adaptive thinking is on by default for this model generation, the model automatically evaluates the task and decides how much reasoning it needs before producing a visible response, rather than skipping straight to output generation the way the same unconfigured request would have on Sonnet 4.6.

The amount of reasoning performed is shaped by the configured effort level, defaulting to high if also unspecified, so the actual depth of thinking on this particular request is a function of both the adaptive decision the model makes about the task's difficulty and the effort ceiling that bounds how far it can reason.

As reasoning proceeds, thinking tokens are generated and counted against the same max_tokens budget that also covers the eventual visible response text - there's no separate token allowance carved out for thinking specifically, so the two draw from one shared pool over the course of the request.

Once the model concludes its reasoning process, it produces the final visible response, and the completed API response includes both the thinking content and the response text, with the request's total token usage reflecting the combined cost of both stages rather than the response text alone.

If a developer wants to restore the old Sonnet 4.6 behavior for a specific request, the fix is to explicitly pass thinking: {"type": "disabled"}, which skips this entire reasoning stage and routes the request straight to response generation, similar to how every request behaved by default before this model generation.

flowchart LR
  A[Request sent, no thinking field set] --> B[Adaptive thinking on by default]
  B --> C[Model evaluates task difficulty vs effort level]
  C --> D[Thinking tokens generated, drawn from max_tokens budget]
  D --> E[Visible response generated, same shared budget]
  E --> F[Response returned: thinking content + response text]

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