I’m working with OpenAI’s language models and running into a token counting problem. Different models like Davinci have specific token limits (for example, 4096 tokens total).
The API has a max_tokens setting that controls how long the response can be. But here’s my issue - I need to know how many tokens are in my input text before sending the request. This way I can calculate the right max_tokens value by doing something like max_tokens = total_limit - input_tokens.
Right now I can’t generate text properly for different sized inputs because I don’t know the token count ahead of time. I want to keep generating until I hit a stop word.
Two things I need help with:
- What’s the best way to count tokens in my input text using Python before sending to the API?
- Can I somehow set
max_tokens to use the full available space without manually calculating?
Any code examples would be really helpful!
I’ve been dealing with this exact issue for months in production. The OpenAI cookbook actually has a solid implementation using tiktoken that handles different model encodings properly. What I found crucial is that you need to account for the system message tokens too, not just your prompt text. My approach is to calculate total tokens from system + user messages, then set max_tokens to leave some buffer space rather than using the absolute limit. Something like available_tokens = model_limit - input_tokens - 50 works better in practice since the API can be finicky when you’re right at the edge. For the second part of your question, there’s no built-in way to auto-calculate max_tokens unfortunately, you have to do the math yourself.
tiktoken is what you want for counting tokens accurately. just pip install it and use tiktoken.encoding_for_model('gpt-3.5-turbo') then call encode() on your text. way more reliable than estimating with character counts or other methods i’ve tried before.
Been working with token counting for about a year now and one thing that caught me off guard initially was the difference between cl100k_base encoding used by newer models versus older ones. For your Python implementation, you’ll want something like enc = tiktoken.get_encoding('cl100k_base') followed by len(enc.encode(your_text)). What really helped my workflow was creating a simple wrapper function that takes the model name and text, returns the token count, then I can do the subtraction math reliably. Also worth noting that special tokens and formatting can add unexpected overhead, so I typically subtract an extra 100-200 tokens from my calculated max_tokens rather than cutting it close. The API will throw errors if you exceed limits and debugging that in production gets annoying fast.