tokencost

0 LLMs. Really bonkers. I know you gotta read the whole method to get it, but, people really shouldn't have just been like "my word! its mean to say they don't get it!" -- it's horrible. https://github.com/AgentOps-AI/tokencost/blob/e1d52dbaa3ada2...

okenizers, long deprecated, used only for deprecated models not served anymore, thus you're not calculating costs with them. The only live ones are c100k and o200k. As described above, and shown in [1], their own code kneecaps the o200k and will use c100k # Explicating #2 Let me know what you'd want to see if you're curious about the 30%+ error thing. I don't want to guess at a test suite that would make you confident you need to revise a prior that there's only +/- 10% difference between arbitrary tokenizers. For context, I run about 20 unit tests, for each of the big 5 providers, with the same prompts, to capture their input and output token counts to make sure I'm billing accurately. # Conclusion Just to save you time, I think the best way I can provide some value is token count experiments demonstrating error. You won't be able to talk me down to "eh, lets just say its +/- 10%, thats good enough for most people!" --- It matters, if it didn't, they'd explicate at least some of this. Instead, its "tokenization for 400 LLMs!" [1] https://github.com/AgentOps-AI/tokencost/blob/e1d52dbaa3ada2...*

sation out of control and convince no one (often persuading them of the opposite of whatever you were trying to say). > just C100K It's not just C100K though. It is for a few models [0], but even then the author does warn the caller (mind you, I prefer mechanisms like an `allow_approximate_token_count=False` parameter or whatever, but that's not fraud on the author's part; that's a dangerous API design). Going back to the "tone" thing, calling out those sorts of deficiencies is a great way to warn other people, let them decide if that sort of thing matters for their use case, point out potential flaws in your own reasoning (e.g., it's not totally clear to me if you think the code always uses C100K or always uses it for a subset of models, but if it's the former then you'd probably be interested in knowing that the tokenizer is actually correct for most models) and discuss better API designs. It makes everyone better off for having read your comment and invites more discussions which will hopefully also make everyone better off. > outside the realm of even needing a function Maybe! I'd argue that it's useful to have all those prices (especially since not all tokens are created equally) in one place somewhere, but arguing that this is left-pad for LLM pricing is also a reasonable thing to talk about. > it's impossible to get definitive error bars That's also true, but that doesn't matter for every application. E.g., suppose you want to run some process on your entire corporate knowledge-base and want a ballpark estimate of costs. The tokenizer error is on average much smaller than the 30%+ you saw for some specific (currently unknown to us here at HN) very small input. Just run your data through this tool, tally up the costs, and you ought to be within 10%. Nobody cares if it's a $900 project or a $1300 project (since nobody is allocating expensive, notoriously unpredictable developers to a project with only 10-30% margins). You just tell the stakeholders it'll cost $2k and a dev-week, and if it takes less then everyone is happily surprised. If they say no at that estimate, they probably wouldn't have been ecstatic with the result if it actually cost $900 and a dev-day anyway. [0] https://github.com/AgentOps-AI/tokencost/blob/main/tokencost...

rce it to help developers avoid nasty bills.