Thinking mode is a provider-specific setting that lets a model spend dedicated computation on intermediate reasoning before producing its answer. It is commonly exposed as thinking blocks, hidden reasoning, a token budget, or an effort control.
Thinking mode is not the same as asking the model to show chain-of-thought reasoning. Modern systems may use hidden or summarized reasoning while returning only a concise final answer. The important operational effect is that the model can allocate more work to decomposition, verification, and tool-use decisions.
Thinking mode affects:
- output-token usage and billing;
- latency;
- context window pressure;
- prompt caching behavior;
- tool-use loops; and
- whether prior reasoning state must be preserved across turns.
It should be used deliberately. For simple tasks, thinking mode can waste tokens. For complex coding, math, analysis, or long-horizon tasks, it can improve reliability if the application measures outcomes.
Anthropic documents thinking configuration, token accounting, and tool-use behavior in its Thinking guide.
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