# Reasoning Model

> A reasoning model is an LLM trained to think before answering — generating internal reasoning tokens it can spend adaptively on hard problems.

**A reasoning model is a language model trained to deliberate before responding — it generates internal "thinking" tokens that work the problem, then produces the answer, spending more thinking on harder problems.**

The line of models that began in late 2024 turned [chain-of-thought](/glossary/chain-of-thought) from a prompting trick into an architecture: reinforcement learning taught models that extended deliberation should change conclusions, not just narrate them. The practical consequence is **test-time compute as a dial** — the same model can answer instantly or think for thousands of tokens, trading latency and cost for reliability on hard problems. Modern frontier models blend the modes, with thinking budgets that adapt or can be set explicitly.

For builders the implications are concrete: thinking tokens are billed [output tokens](/glossary/llm-token), so reasoning tiers change your [cost envelope](/guides/advanced/llm-cost-latency-engineering); prompts written for older models ("think step by step") may be redundant; and tier selection — when deliberation pays versus when it's overhead — becomes a real engineering decision, the same one [Choosing the Right Model](/guides/getting-started/choosing-the-right-model) walks through for Claude's tiers.

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_Source: https://agentscamp.com/glossary/reasoning-model — Term on AgentsCamp._