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You're building an LLM application that uses multiple providers. You need to count tokens accurately and manage rate limits. But here's the nightmare:
OpenAI has tiktoken. Different API, different methods. Meta's Llama has HuggingFace tokenizers. Another integration. Claude? No public tokenizer. You have to call the API just to count tokens. Gemini? Same problem. API-only, proprietary. Groq, DeepInfra, Together? They all count differently. Good luck.
You end up with:
❌ 10+ different libraries to install
❌ Different APIs for each provider
❌ Expensive API calls just to count tokens (200-500ms each)
❌ No consistent way to get token counts
❌ Wasted time integrating providers instead of building features
This is madness. You just want to count tokens.
The Solution: PyTokenCalc - pip install pytokencalc
One library. One API. All providers.
Finally a token counter that works for local models and weird custom endpoints. Tested it against tiktoken and a couple of Ollama setups, numbers matched perfectly and the pattern fallback is genuinely clever for models without official tokenizers.
finally a tokenizer wrapper that actually works with the local models I run, not just openai. dropped it into my pipeline and the counts matched my hand checks within rounding.
handy little lib, dropped it into a quick script and it just worked across openai and a local llama model without any extra config.
One thing that would make this way more useful for me is a CLI mode or simple Python one-liner so I can quickly count tokens from the terminal without writing a full script. Something like `pycount "text here"` or piping from stdin would be perfect for quick checks.
Finally a token counter that works across all my models without juggling different libraries. Tested it against a few cloud APIs and the numbers matched perfectly, way less hassle than I expected.
About PyTokenCalc on Product Hunt
“About Universal token counting for ANY LLM. ”
PyTokenCalc was submitted on Product Hunt and earned 17 upvotes and 7 comments, placing #76 on the daily leaderboard. Universal token counting for ANY LLM. 20+ providers (cloud + local), custom models, pattern-based forward-compatibility, 99%+ accuracy. - Mullassery/PyTokenCalc
PyTokenCalc was featured in Artificial Intelligence (474.3k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 134.1k products, making this a competitive space to launch in.
Who hunted PyTokenCalc?
PyTokenCalc was hunted by Georgi Mullassery. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Georgi Mullassery