Lesson 84 of 1570
Reasoning effort — when to pay for deeper thinking
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.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The o-series lesson still matters
- 2Pay for deeper reasoning when
- 3Skip it when
Concept cluster
Terms to connect while reading
Section 1
The o-series lesson still matters
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.
Section 2
Pay for deeper reasoning when
- You have a hard, discrete problem with a verifiable answer
- Latency budget is in minutes, not seconds
- A single good answer saves a human 30+ minutes
- Proofs, plans, derivations, or multi-file code changes are the work
Section 3
Skip it when
- Chat that needs snappy turns
- Simple retrieval or rewriting
- Creative writing where voice matters more than proof
- Tasks GPT-5.4 mini already solves reliably
Compare the options
| 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 | $ | $$$ |
Reasoning effort is a budget dial. Treat high and xhigh as deliberate choices, not defaults.
response = client.responses.create(
model="gpt-5.5",
reasoning={"effort": "high"},
input=hard_problem,
)Key terms in this lesson
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