Verified70% confidenceTradeoffExact time
The 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.
4
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70%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
Related Claims
Verified思考模型在输出前会经历内部链式推理（Chain-of-Thought）过程，在数学证明、代码调试、复杂逻辑推理等任务上表现优于标准模型，代价是延迟更高、计算成本更大83% similarUnverifiedThe total inference cost of large models exceeds the training cost, because inference continues to occur and grows linearly with user scale.81% similarVerifiedReasoning Effort is essentially a parameter that controls the depth of the model's chain of thought; the higher the level, the more internal reasoning steps the model is allowed, and latency and cost rise proportionally.81% similarUnverifiedExtended thinking mode causes the model to perform an explicit internal reasoning chain before output, at the cost of significantly increased token consumption, with reasoning tokens typically billed at a higher rate.80% similarUnverifiedReasoning models (such as the o1 series) can cost several times to more than ten times more than regular models because they need to generate a large number of intermediate thinking steps.79% similar
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