Reasoning Model
A language model trained to think before it answers — generating a long internal chain of reasoning and spending extra compute at inference to solve harder problems.
Reviewed July 16, 2026Contested
When not to use it
- For quick factual, conversational, or creative tasks, where the extra latency and cost of deliberation buy nothing.
- When speed matters more than a marginal accuracy gain, since reasoning models are inherently slower per answer.
- When the task has no checkable structure and you cannot tell whether the extra reasoning actually helped.
Reach for something else instead
- Standard (non-reasoning) LLMs for the majority of everyday tasks, where immediate answers are fine and cheaper.
- Prompted chain-of-thought on a standard model, which captures some of the benefit without a specialised reasoning model.
- External tools and verifiers (calculators, code execution, search) that offload exact steps rather than reasoning them internally.
Sources & further reading
- OpenAI (2024), Learning to Reason with LLMs — the o1 announcement that popularised inference-time reasoning.
- DeepSeek-AI (2025), DeepSeek-R1 — an open reasoning model trained largely with reinforcement learning on verifiable rewards.
- Snell et al. (2024), Scaling LLM Test-Time Compute Optimally — evidence that inference compute can outperform added parameters.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Using a reasoning model for everything, paying for deliberation on tasks that do not need it.
- Reading the reasoning trace as a faithful explanation of how the answer was reached; it may not be.
- Assuming reasoning ability trained on maths and code transfers cleanly to open-ended judgment tasks.
At a glance
FieldLanguage & LLMs
Core ideathink before answering
Mechanismtest-time compute + RLVR
Strengthmulti-step problems
Costslower, pricier per query
DifficultyIntermediate