Term
Reasoning Effort
Reasoning Effort is a control parameter on modern reasoning models that sets how much internal step-by-step thinking (thinking tokens) a model spends before answering — higher levels raise quality but also latency and cost.
Reasoning Effort — explained in more detail
Reasoning Effort is a parameter that controls how deeply a modern reasoning model thinks internally before producing its actual answer. These models generate so-called thinking or reasoning tokens ahead of the visible output — an internal chain of intermediate considerations that the user usually does not see, or sees only as a summary. The effort parameter determines how much budget the model is allowed to invest in this internal phase.
The value is typically set through discrete levels. OpenAI uses reasoning_effort on the GPT-5 models with levels ranging from minimal through low, medium, high and beyond (in GPT-5.4 the span runs from none to xhigh). Anthropic, on the Claude models (from Opus 4.6 / Sonnet 4.6 onward), replaced the earlier fixed token budget (budget_tokens) with an effort parameter and adaptive thinking: within the chosen level, the model dynamically decides how much it actually thinks.
The core trade-off is quality versus latency and cost. More effort means more reasoning tokens, longer response times and higher billing (reasoning tokens are charged like output tokens), but it noticeably improves accuracy on demanding tasks — math, multi-step logic, code, scientific or domain questions. On simple tasks, high effort is pure waste: the answer barely improves while taking longer and costing more.
Example / Practical context
For a simple formatting or extraction task (“pull the e-mail address out of this text”) you pick minimal or low — the task needs no extended thinking, and a low level keeps response time and cost down. For a multi-step analysis, an architecture decision or a tricky math/code task, high pays off, because the model can work through more internal steps and arrives at the correct solution more reliably.
In practice you therefore set effort per request to match the task rather than globally high: a productive workflow uses low levels for routine calls and only steps up for the hard parts. That keeps average latency low without sacrificing quality on the genuinely difficult cases.
Distinction from similar terms
Thinking budget / budget_tokens is the more concrete, older knob: a hard ceiling on how many thinking tokens the model may spend at most. Reasoning Effort is more abstract — a level that acts as soft guidance; the model uses the budget within that level adaptively. At Anthropic, the effort parameter has largely replaced the fixed budget.
Temperature controls something entirely different: the randomness in token selection (see also Sampling). Low temperature makes output more deterministic, high temperature more creative — it says nothing about how much the model thinks, only how variable its next-token choice is. Effort and temperature are orthogonal controls and are often combined.
Max tokens caps the length of the visible output, not the depth of the internal reasoning — though on reasoning models thinking tokens count toward the overall token limit, which is why both parameters have to be planned together.
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