Verified70% confidenceFactTime unknown
Output tokens are more expensive because the generation process requires autoregressive inference token by token, with each token generation requiring a full forward computation.
4
Sources
70%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
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Related Claims
UnverifiedIn autoregressive generation, each token requires a full forward computation, and inference latency is proportional to generation length, making it the main throughput bottleneck.74% similarUnverifiedFor large models, "inference efficiency" usually refers to the number of tokens that can be generated per unit time (throughput) and the first-token latency.72% similarUnverified大模型每个数字都是逐 token 生成的，而非逐位计算，导致其算术能力在小数、除法、多步混合运算上不稳定70% similarUnverifiedUnder multi-model architectures, the billing model is harder to predict: when billing by token, the same input sentence may produce several times the price difference depending on the routing result; if unified account settlement is used, costs are smoothed out, but the product side must bear the losses caused by routing mistakes itself.70% similar
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