Reasoning Model
An LLM trained or designed to spend extra compute on hard problems before answering.
Reasoning models internally think through a problem before producing an answer. Examples include OpenAI's o1 and o3, Claude with extended thinking, and Google's Gemini Thinking. They're slower and cost more than standard models, but they perform meaningfully better on tasks that benefit from sustained reasoning — math, complex code, multi-step planning, scientific analysis.
For operators, the rule of thumb is: use a reasoning model when getting it right matters more than getting it fast or cheap. Use a standard model when latency or cost is the constraint and the task is straightforward.
A pragmatic pattern: route requests dynamically. Hard cases go to a reasoning model; easy cases go to a fast standard model. Many production AI products in 2026 do this transparently to manage cost.
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