Reasoning
Models that think before answering — a large real capability gain, and the visible thinking is not a reliable account of what happened.
When not to use it
- On easy questions. You pay for tokens that don't help, and the model can talk itself out of a correct answer.
- On retrieval, summarisation, extraction or formatting. There's nothing to reason about.
- As an explanation of the model's behaviour. Turpin et al.: the trace can be a rationalisation that never mentions what actually drove the answer.
- Where answers aren't verifiable. The training method needed a verifier; the capability follows the verifier.
Reach for something else instead
- A cheap model plus routing — decide which questions are hard. That's where the savings are.
- Chain-of-thought prompting — free, works on any model.
- Tool use — a calculator beats reasoning about arithmetic.
- Best-of-n with a verifier — if you can check answers, checking several is often better than thinking harder about one.
Sources & further reading
- Wei et al. (2022), Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — where the capability gets named.
- Turpin et al. (2023), Language Models Don't Always Say What They Think — chains of thought as post-hoc rationalisation. The essential result.
- DeepSeek-AI (2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning — pure RL on verifiable rewards; reasoning behaviours emerging unprompted.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reading the trace as an explanation. It's more generated text, from the same process, with the same failure modes.
- Using a reasoning model for everything. It's expensive, slow, and sometimes worse.
- Asking a reasoning model to explain its reasoning. You get a rationalisation of a rationalisation.
- Expecting the maths gains to transfer to judgement tasks. The method needs a verifier and those don't have one.
At a glance
FieldLanguage & LLMs
Mechanismgenerated tokens are computation
How it's trainedRL on verifiable rewards; no human reasoning traces needed
Why maths and code specificallythose are the domains with cheap verifiers
The essential caveatthe trace is not the computation (Turpin et al.)
DifficultyIntermediate
Flashcards for this concept
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Often compared with
Reasoning model vs. standard model — one spends thousands of tokens computing before it answers and produces a persuasive trace of what it didn't necessarily do.