Unverified50% confidenceFactExact time
带有Chain-of-Thought推理功能的Thinking模型，由于需要生成大量中间推理token，其实际token消耗量可能是普通对话的5-20倍
1
Sources
50%
Confidence
Long-term
Relevance
8/26/2026
First Seen
Sources
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Related Claims
UnverifiedReasoning models perform extended reasoning within thinking blocks wrapped in special tokens (such as <think> and </think>); this content is typically not shown directly to users but consumes a large number of tokens.76% similarUnverifiedThe internal chain of thought in reasoning models generates a large number of output tokens, making the actual cost of completing complex tasks often far higher than what a per-token price comparison would suggest.76% similarUnverified使用包含引导语的Chain-of-Thought提示，可使大语言模型在复杂推理任务上准确率提升40%以上75% 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73% similarUnverifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.73% similar
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