When should an agent use extended thinking or a higher effort setting, and what does it cost?

Use more thinking or a higher effort level for hard steps where reasoning changes the outcome: planning a multi-step task, debugging, analysis with many constraints, maths and code. Use less for routine steps: classification, extraction, simple lookups and formatting. The cost is more output tokens, which means higher price and latency, so set it per task from your evaluation results, not once for everything.

How the controls work today (Claude API)

• Effort (output_config.effort) tells the model how many tokens to spend overall: on text, on tool calls and on thinking. Anthropic's documentation says most Claude models default to high, Claude Opus 5.5 and Claude Haiku 5.5 default to medium, and some models offer max for the highest capability. Lower effort also means fewer and terser tool calls, which matters in agents.
• Adaptive thinking lets the model decide how much to think. The older manual mode with a fixed budget_tokens is deprecated on the 4.6 models, and Claude 4.7 and later reject it with a 400 error, so code written for older models may need updating.
• Anthropic's model-choice guide notes that tuning effort is often a better lever than switching models.

Other providers have similar controls, such as reasoning effort settings on reasoning models.

Where more thinking pays in an agent

• The planning step at the start of a complex task.
• Recovering from errors, when the obvious approach has failed.
• Decisions with consequences: choosing what to change in a codebase, or whether to escalate.
• Tasks your evaluation shows fail at lower effort.

Where it is waste

• Routing and classification.
• Pulling fields out of a document into a schema.
• Steps a smaller, faster model already handles at your quality bar.

How to decide

Run the evaluation set at two or three effort levels and compare success rate, tokens per task and latency. Often the curve flattens: medium gets most of the quality of high at a fraction of the tokens for easy tasks, while hard tasks keep improving. You can also split by step: a high-effort planner with low-effort workers.

Watch the budget

Thinking tokens are billed as output tokens. Set max_tokens high enough for thinking plus the answer, and keep turn and cost limits on the agent as a whole.

What interviewers want: that you treat reasoning depth as a tunable cost, measured per task, and know that the API surface for it changes between model generations.