A concrete example
GPT-5 reasoning low = 5 seconds, medium = 30 seconds, high = 2 minutes. I tune it to the complexity.
Why it matters
Setting reasoning effort right means finding the cost-quality-latency balance. Cranking it to the max on simple tasks is throwing money away.
You run into it in reasoning models, APIs, Gemini thinking, Claude extended thinking, and some Codex settings.
Don't mix it up with
Reasoning model: A reasoning model takes more time to think before answering a complex task.
Reasoning tokens: Reasoning tokens are the ones some models burn while thinking before they produce the visible answer.
Common mistakes
- Setting reasoning high by default everywhere.
- Forgetting that reasoning effort multiplies the reasoning tokens you're billed for.
- Thinking high effort guarantees a better answer (sometimes it's the opposite).
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: GPT-5 reasoning low = 5 seconds, medium = 30 seconds, high = 2 minutes. I tune it to the complexity.
- I keep the main trap in mind: Setting reasoning high by default everywhere.
Quick questions
What is Reasoning effort in AI?
Reasoning effort sets how much effort the model puts into thinking before it answers.
Where will I run into Reasoning effort?
You run into it in reasoning models, APIs, Gemini thinking, Claude extended thinking, and some Codex settings.
Which word should I read next?
Start with Reasoning model, Reasoning tokens, Chain of thought.