The total number of tokens counted at playground is 209, and the bill shows 200, which is playground link
https://platform.openai.com/playground/p/59GDMkeYd2zoiVauA2NaCvqQ?model=text-davinci-003
I tested it over and over again, and both the Tokenizer and playground calculated differently from the bill. I searched for forum posts with similar issues to mine. It happened two years ago and the problem is still not resolved
Links to previous questions
Can you share the exact prompt you used. Because if it’s just the Human: Use java to create a socket server, code how to write? I get 16 tokens, which is not what the billing shows. Could be that it pertains to a different completion call.
And If I were to remove the trailing space, the prompt is just 15 tokens.
Thank you for your reply. There seems to be a bug in the sharing link of the playground. The calculation of tokens should include AI:
Complete content
Human: Use java to create a socket server, code how to write?
AI:
It totals to 212 tokens.
And if I were to use code series models the token count comes to exactly 200, which corresponds to your billing statement.
Yes, the code model seems to be correct, but the calculation of the text-davinci-003 model is wrong. I have been studying this problem for two days
No the calculation isn’t wrong. The codex series models use a different tokenizer than the text series.
Sorry I didn’t understand you, have you used the text-davinci-003 model? Is it consistent with the bill? I really don’t know how to accurately calculate the number of tokens used by text-davinci-003
Thank you very much for your patient answer. I tested it again and found that the calculation of tokens is still inaccurate.
It can be seen that the tokens of the question are accurate, and 14 tokens are consumed
But the answer is wrong, a total of 88 tokens, 88-14=74, the bill shows 14+60=74
This is playground
The calculation is correct for normal text, but wrong for code calculation
Exactly i experienced the same problem!
I conducted a second test, this time I set the maximum token to 1000, the tokens calculated by the question were correct, but the answer was wrong, and the number gap was very large
I reverse engineered the actual javascript code and data files on the Tokenizer site and noticed a big difference as well
The tokenizer page uses a different encoder.json and vocab.bpe file.
The number of tokens are the same, but the order and the values are different when it comes to unicode characters (and several other places)
It also has a different regex expression in the javascript function. (Notice the second pattern handles all the Unicode characters)
string pat = @"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+";
vs
string pat = @"'s|'t|'re|'ve|'m|'ll|'d| 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?(?:(?![\t-\r 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\xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000\uFEFF]+(?!(?:[\x00-\x08\x0E-\x1F!-\x9F\xA1-\u167F\u1681-\u1FFF\u200B-\u2027\u202A-\u202E\u2030-\u205E\u2060-\u2FFF\u3001-\uD7FF\uE000-\uFEFE\uFF00-\uFFFF]|[\uD800-\uDBFF][\uDC00-\uDFFF]|[\uD800-\uDBFF](?![\uDC00-\uDFFF])|(?:[^\uD800-\uDBFF]|^)[\uDC00-\uDFFF]))|[\t-\r \xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000\uFEFF]+";
I pointed this out a few weeks back, and have spent many hours trying to discover the correct formula. You are correct that it appears the tokenizer page doesn’t match the final bill.
At first I though it was the inclusion of the [END] token on the streaming output. But when you deal with foreign languages, the differences are very large.
I gave up. If anyone figures it out, I would be really appreciative.
We don’t have access to the formulas used by the billing component. My comparisons wee against the published tokenizers on huggingface for gpt2/gpt3 and the actual javascript used on the tokenizer page. They are completely different.
I found a method, which can make the calculation error not exceed 10 tokens, I only tested Chinese and English
curl --location --request POST 'http://api.ppgjx.com/app/func/chatgpt/getTokens' \
--header 'Content-Type: application/x-www-form-urlencoded' \
--data-urlencode 'context=Use java to create a socket server, how to write the code?
import java.net.*;
import java.io.*;
public class SocketServer {
public static void main(String[] args) throws IOException {
// Create a server socket and wait for client connections on port 8888
ServerSocket serverSocket ='
Yeah, I did a port of the tokenizer into Swift a while back, and noticed that there is no difference between Text and Code in OpenAI’s Tokenizer page - they both use identical encodings.
hello, any updates on this issue? looks like it still happening so i am unable to calculate prompt tokens upfront.
i found an issue on openai-java project but looks like the issue is in the api itself, not in the wrapper
Is the difference a fixed offset each time or is there variability to it? I’m thinking perhaps you have not taken into account internal system boundary marker tokens, “finish_reason” and return code, things like that.
Hello Foxabilo, thanks for the reply!
So there IS indeed extra prompt tokens, i’ll look in the docs for them, but if you have a link in hand i thank you again!
once i find a detailed info i’ll be back again with more feedback, that token count issue might be hitting more people.
Not sure if it’s in the docs, I do remember seeing it spoken about with a group of OpenAI early adopters and they were having a detailed discussion about the the API mechanics and they had the source code from github GitHub - openai/openai-python: The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language. and they were looking at boundary markers in the return JSON stream and creating a more accurate token counter system. So on the topic of “are there any additional tokens used” the answer is 100% yes, there may well be additional details on the forum if you search for them 
this may be of potential interest?
Might not be it exactly, but it’s in the same ballpark.
I’ll take a detailed look at the provided link, thanks a lot!
The solution seems to use some heuristics on well known prompt payloads, it’s a good start!