Verified65% confidenceFactTime unknown
The 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.
3
Sources
65%
Confidence
Long-term
Relevance
5/31/2026
First Seen
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Related Claims
VerifiedThe inference cost of large language models is variable and usage-based; a single Agent handling a complex task may trigger dozens of model calls and consume millions of tokens.82% similarVerifiedEach model response includes the full conversation history (context window) in billing, and as the number of dialogue turns increases, the token consumption per call grows linearly or even super-linearly.79% similarUnverifiedThe cost structure of large language models includes a massive one-time investment during the training phase and ongoing operational costs during the inference phase.78% similarUnverifiedThe inference time and cost of large language models are approximately linearly correlated with the number of input tokens.76% similarUnverifiedThe higher the embedding dimension, the richer the semantic information the model can encode, but the costs of storage, indexing, and similarity computation also grow linearly—this is a trade-off.75% similar
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