What is tokenizer-benchmark?

Tokenizer Benchmark is a small Python tool that measures how efficiently different tokenizers encode text in multiple languages. A tokenizer is the part of a language model that breaks text into pieces before the model processes it, and how well it handles a given language directly affects cost and speed. This project lets you compare tokenizers side by side on the same word lists to see, for example, which one handles Hindi or Punjabi more efficiently, or how much better a newer OpenAI encoding is than an older one. The tool works with two families of tokenizers: TikToken, which covers OpenAI's encodings such as gpt2 and o200k_base, and Hugging Face, which covers any tokenizer available through the transformers library, including custom models with their own vocabularies. You choose which tokenizers and languages to compare through a JSON configuration file, and the benchmark runs each one against word lists organized by dataset, such as a common-words list or a set of programming terms. If one of the chosen tokenizers fails to load, the benchmark skips it and keeps going rather than stopping the whole run. Datasets are simple text files, one per language, where each file holds the same words in the same order so that a row lines up in meaning across languages. Adding a new language means creating one more text file with matching word order, no code changes needed. The same applies to adding a new dataset for a different domain such as medical or legal terminology. Results can be printed to the console or exported as TXT and CSV files, making it easy to compare numbers across many languages and tokenizers in one table. The project is still in an early state, structured as a modular Python package with separate pieces for loading datasets, building tokenizer instances, running the benchmark, and generating reports, so new tokenizer types can be added without touching the rest of the code. Setup involves cloning the repository, creating a Python virtual environment, and installing dependencies, though the maintainer notes the dependency list has not been filled in yet. This is aimed at people building or evaluating multilingual language models who want a repeatable way to measure tokenizer efficiency rather than guessing from anecdotal examples.