Unverified50% confidenceTradeoffExact time
When using a generative model to evaluate K candidate options, the total cost grows linearly with the number of options, whereas a single forward pass can compress the inference cost to a single computation
1
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50%
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Long-term
Relevance
10/11/2026
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Related Claims
UnverifiedEach output token requires a full autoregressive inference computation, and the cost grows linearly or even super-linearly with length.76% similarVerifiedDense架构中模型所有参数在每次推理时都参与计算,计算成本随参数规模线性增长,以Llama系列为代表74% similarUnverifiedGenerative decision models must process the current context together with all candidate options on every call, with the computational load increasing as the number of options grows.73% 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.73% similarUnverifiedDiffusion model inference requires a fixed number of forward passes with each pass acting on the entire sequence, making the total cost a function of the number of steps multiplied by the sequence length.72% similar
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