Unverified50% confidenceFactExact time
The inference time and cost of large language models are approximately linearly correlated with the number of input tokens.
1
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50%
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Long-term
Relevance
9/22/2026
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UnverifiedThe inference process of large language models is autoregressive, and the number of output Tokens is almost linearly correlated with response latency.79% similarUnverified大语言模型每次调用的费用与输入token数量正相关，在Agent循环中随着对话历史积累每次调用成本会线性增长76% similarVerifiedThe computational cost of large language models depends primarily on the number of tokens, model parameter scale, and context window size.75% similarUnverified大型语言模型的推理成本与参数规模并非线性关系，参数量从千亿级跃升至万亿级时推理所需的GPU显存、带宽和计算量会指数级攀升73% similarUnverifiedReasoning mode performs internal chain-of-thought reasoning before the final answer, and the thinking tokens generated are billed; in complex programming tasks, thinking tokens may account for 60%–70% of total consumption, and costs may be 3–5 times that of normal mode.73% similar
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