LLMs generate text token-by-token, driving up output costs

Large language models generate text token by token, a process known as autoregressive generation, which makes output significantly more expensive than input processing. Unlike the parallelized input phase, generating each subsequent token requires a sequential forward pass through the model, as each new token depends on the previously generated ones. This sequential nature is the primary reason why output tokens are approximately four times more costly than input tokens, impacting prompt design, API costs, and UI development. AI

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IMPACT Explains the fundamental cost asymmetry between LLM input and output, impacting developer strategies and API pricing.

RANK_REASON Explains a core technical mechanism of LLM operation and its cost implications. [lever_c_demoted from research: ic=1 ai=1.0]

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