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Basic concepts

Prompting
Capabilities
Current systems
Algorithms
Alignment concepts
Intelligence and optimization
AI goals
Risks and outcomes

What are "reasoning" AI models?

“Reasoning” AI models1

are LLMs that spend some time and compute tokens “thinking” before answering queries. Examples include OpenAI’s o1 and o3, DeepSeek’s R1, Anthropic’s Claude 3.7, and Google DeepMind’s Gemini Flash Thinking.

These models use a process similar to chain-of-thought prompting

to reflect on their own output. This allows them to perform better on tasks such as analytical reasoning.

DeepSeek’s R1 reflecting on a query

Reasoning models require substantially more compute than non-reasoning models, which increases both their cost per token and their environmental impact.


  1. As of Q1 2025, these models are rather new and there is no standardized name for this type of model. They have been called simulated reasoning models, chain-of-thought models, large reasoning models and enhanced reasoning models. ↩︎

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