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Reasoning models

Reasoning models are language models trained to work through a problem in intermediate steps, sometimes called "thinking," before giving a final answer. The approach improves results on math, coding and multi-step tasks, but the extra tokens add cost and latency.

11 stories · updated Oct 9

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Key facts

Checked Oct 9

Chain-of-thought prompting: Wei et al., Jan 28, 2022
OpenAI o1 / "Learning to reason with LLMs": September 12, 2024; RL on chain of thought; scales with test-time compute
DeepSeek-R1: Jan 22, 2025; reasoning via reinforcement learning
Claude thinking controls: budget_tokens (extended thinking): Claude 4.5 and earlier; deprecated on 4.6; rejected (400 error) on 4.7 and later, incl. the 5.5 models and Fable 5.1, which use adaptive thinking plus effort. Thinking counts toward max_tokens and billed output tokens

Latest on Reasoning models

Models · ExplainerReasoning models explained: thinking tokens, what they cost, and when to use themClaude, GPT-6 and Gemini all bill hidden thinking as output tokens. How each vendor exposes it, what it costs in practice, and when to turn it down.Oct 9·7 min read

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