> the measurements of each LLM

I think you have the correct idea.

If you ask an LLM to solve a multiplication problem using reasoning without code tools, depending on the model, it will get 15 digit (15D) * 15 digit multiplication correct (123456789012345 x 998765432109876). It will take between 4000 and 8000 tokens if Sonnet. Eventually with enough digits, it will start only solving the problem 80% ... then 70% of the time until it has so many digits it will never solve. There will be a certain number of digits where it will not converge on a solution nor will it stop working thinking it can solve it. That is very, very expensive. It takes a lot of runs to determine the probability it will solve it.

What you can do now, this is likely the most important thing, is change the prompt and evaluate how many tokens and at what speed it takes to accomplish the task. Sure the measurements of each LLM are important! Nonetheless, if you can say to a company that you have a technique to tune prompts and evaluate them so instead of spending $1 X 100,000 times a day, they can instead spend $0.90 X 100,000 times a day, you will make a ton of money.