Price per 1M tokens is meaningless
The article argues that comparing AI models based on price per million tokens is misleading due to inconsistent tokenization methods across labs. It suggests that hidden costs like 'chain of thought' processing are more significant factors in actual AI expenditure.
Why it matters
Businesses need more accurate metrics to manage AI operational costs effectively as tokenization strategies vary widely.
It stops being all about the vibes when the API bill hits you. Many companies are now discovering that AI can indeed be pricey. One habit that might be driving up your AI bill is comparing models by $X per 1M tokens . A lower number should mean lower costs, right? Well, not really.
The analysis is technical and industry-focused, providing objective critique of current pricing models.
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