Adaptive thinking replaces extended thinking — on by default in Claude Code and the API. The model decides when to think; you set how hard. Drag the reasoning core to orbit; change effort to reshape it.
Before answering, the model can emit an internal chain of thinking tokens — scratch-work it uses to plan, check edge cases, and self-correct. You pay for them like output tokens, and they add latency before the visible answer starts.
More thinking helps on genuinely hard steps (multi-file reasoning, tricky math, long plans). It adds nothing on trivial ones.
Extended thinking was a switch plus a fixed token budget: you guessed up front how much scratch-work every request needed.
Adaptive thinking is on by default in Claude Code and the API. The model decides per-step whether to think at all; your effort setting (low / medium / high) only biases how deep it goes when it does. No budget guessing.
| Task | Effort |
|---|---|
| Quick edits, renames, lookups | low / medium |
| Most day-to-day coding | medium (default) |
| Long agentic runs, many tools | high |
| Memory-heavy, cross-session work | high |
Start at medium. Bump to high only when runs are long or state is heavy — most coding won't need more.
Every thinking token is billed and delays the answer. Forcing high effort on a one-line fix can multiply cost and latency for identical output quality.
Rough intuition: cost ≈ input + thinking + output tokens. Only one of those is under a dial — spend it where reasoning depth actually changes the result.
In the API you pass an effort hint alongside your request, e.g. thinking: { effort: "high" } — no more token budgets to size. In Claude Code, adaptive thinking is simply on; the default effort is medium and you only override it for special workloads.
Because the model skips thinking on easy steps, a whole session at high is far cheaper than the old "max budget on every call" pattern — depth is spent only where the model judges it's needed.
You're kicking off an overnight autonomous agent that will chain hundreds of tool calls across a large repo. Which effort do you pick?