I dig it! Kind of related, but I made a comparison of LLM API costs vs their leaderboard performance to gauge which models can be more bang for the buck [0]
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Tokencost works by counting the number of tokens in prompt and completion messages and multiplying that number by the corresponding model cost. Under the hood, it’s really just a simple cost dictionary and some utility functions for getting the prices right. It also accounts for different tokenizers and float precision errors.
Surprisingly, most model providers don't actually report how much you spend until your bills arrive. We built Tokencost internally at AgentOps to help users track agent spend, and we decided to open source it to help developers avoid nasty bills.