Unverified50% confidenceFactExact time
A model's compute cost is approximately quadratic with sequence length (due to the attention mechanism), so billing by token reflects real compute consumption
1
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50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
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Related Claims
VerifiedExtended 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.76% similarUnverifiedEach model invocation calculates both input tokens and output tokens simultaneously, and both together determine the cost of a single call.76% similarUnverifiedThe inference time and cost of large language models are approximately linearly correlated with the number of input tokens.74% similarUnverifiedThe context window refers to the total number of tokens a model can see in a single inference, including input and output; the computational cost of the attention mechanism grows quadratically with sequence length.74% similarUnverifiedThe computational overhead during inference is primarily composed of the memory bandwidth consumed by loading model parameters into VRAM and the matrix operation volume in forward propagation.73% similar
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