Loading lesson…
Reasoning effort trades latency and tokens for better answers on hard problems. Here is when that trade is worth it. In the current GPT-5 family, that choice usually shows up as model selection plus a reasoning effort setting.
Older o-series models taught developers the pattern: spend more inference compute when the answer is hard enough to justify it. In the current GPT-5 family, that choice usually shows up as model selection plus a reasoning effort setting. Higher effort can improve math, code, planning, and logic, but it also adds latency and output tokens.
| Task | GPT-5.4 mini low effort | GPT-5.5 high effort |
|---|---|---|
| Routine summarization | Excellent | Overkill |
| Competition math | Useful but uneven | Much stronger |
| Refactor a complex module | Decent | Excellent |
| Latency | seconds | longer seconds or minutes |
| Cost per call | $ | $$$ |
response = client.responses.create( model="gpt-5.5", reasoning={"effort": "high"}, input=hard_problem, )Reasoning effort is a budget dial. Treat high and xhigh as deliberate choices, not defaults.8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-o4-reasoning-builders
What is the main idea of "Reasoning effort — when to pay for deeper thinking"?
Which concept is most central to "Reasoning effort — when to pay for deeper thinking"?
Which use of AI fits this topic best?
What should a careful learner remember about "Do not block a chat UI on hard reasoning"?
You want to use AI after this lesson. What is the safest next step?
How should AI output about reasoning effort be treated?
Name one way to verify an AI answer about reasoning effort.
Which action would help you apply "Reasoning effort — when to pay for deeper thinking" responsibly?