Reasoning (LLM) — AI Glossary

Reference details

Related terms

• Chain of Thought (CoT)
• Tree of Thoughts (ToT)
• MATH Benchmark

Example: Reasoning that costs tokens you cannot see

A reasoning model may generate thousands of internal tokens before its first visible word. You are billed for them and you wait for them, but they never appear in the response. A task that looks like a 200-token answer can be a 4,000-token call — which is why reasoning models change latency and cost profiles, not just quality.

Commonly confused with

Reasoning models and chain-of-thought prompting are not the same thing. Chain-of-thought asks any model to show its steps in the output; a reasoning model was trained to reason internally before answering. Prompting the second for step-by-step output can fight the training rather than help it.

When to use it

Reach for it when:

• The task has verifiable intermediate structure: maths, logic, multi-constraint planning
• Accuracy matters more than latency or cost
• A wrong answer is expensive enough to justify the extra tokens

Reach for something else when:

• Retrieval, extraction, formatting or classification — reasoning adds cost, not accuracy
• The interface is latency-sensitive and users watch a blank space
• You are budgeting on visible output alone, which understates the bill

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