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%
Confidence
Long-term
Relevance
9/22/2026
First Seen
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Related Claims
UnverifiedThe inference process of large language models is autoregressive, and the number of output Tokens is almost linearly correlated with response latency.79% similarUnverifiedThe cost of each large language model call is positively correlated with the number of input tokens, and in an Agent loop, as the conversation history accumulates, the cost of each call grows linearly.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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