Very unpalatable completely LLM-written article, but on top of that a lot of the fundations and conclusions are completely wrong, the main one being this one:

> You will see people claim Claude uses 2x to 4x the tokens of GPT. Our measurements do not support that, and overstating it would undercut the real point.

It's not because a single prompt represents only 1.7x the number of tokens that a model doesn't use 4x as many tokens as another, when running as an agent. This doesn't take at all the number of tokens of the output into account, and the number of tokens of the potential tool calls from this output, which directly feeds back to input tokens.

The article also has a very small test set (16 documents), all of very small length (15K tokens at most, when models go up to 1M in context and agents routinely exceed this and have to summarize).

Complete garbage article.