And, that's silly, because API pricing is more expensive for output than input tokens, 5x so for Anthropic [1], and 6x so for OpenAI!
[1] https://platform.claude.com/docs/en/about-claude/pricing
Large outputs dominate compute time so are more expensive.
IMO input and output token counts are actually still a bad metric since they linearise non linear cost increases and I suspect we’ll see another change in the future where they bucket by context length. XL output contexts may be 20x more expensive instead of 10x.
For Anthropic, as a business bleeding money, it's probably nice to have value-based pricing, for the tokens, so innovation (like computation efficiency improvements) can result in some extra margin. If they exposed the more direct computation cost, they could never financially benefit from any improved efficiency, including faster hardware!
This is a bit too much of a simplification.
The LLM provider batches multiple customer requests into one GPU/TPU pass over the weights, with minimal latency increase.
The LLM provider may in fact be renting GPUs by the second, but the end user isn't. We the end users are essentially timesharing a pool of GPUs without any dedicated "1 vGPU" style resource allocation. In such a setting, charging by "GPU tick" sounds valid, and the various categories of token costs are an approximation of cost+margin.