Unverified50% confidenceFactExact time
Chain-of-Thought reasoning significantly increases generation length: the final answer to a simple math problem may require only 20 tokens, but the intermediate reasoning process may require 200–500 tokens.
1
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50%
Confidence
Long-term
Relevance
9/6/2026
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Related Claims
UnverifiedThinking models with Chain-of-Thought reasoning capabilities may consume 5–20 times the tokens of ordinary conversations in practice, due to the need to generate large numbers of intermediate reasoning tokens.78% similarVerifiedChain of Thought能显著提升模型在数学推理、逻辑判断等需要多步推导的任务上的表现76% similarUnverified链式推理通过引导模型在给出最终答案前显式输出中间推理步骤，某些任务上准确率提升可达20-40个百分点74% similarUnverified2021年的Chain-of-Thought Prompting论文证明，通过在提示词中加入推理步骤示例，模型的数学推理准确率可提升数十个百分点73% similarUnverifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.73% similar
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