Multi-model tokenizer playground
The same sentence can become 18 tokens in one model and 24 in another, and you pay per token — so tokenizer efficiency is a real cost lever. This playground estimates token counts for GPT, Claude, Llama, and Gemini-style tokenizers side by side, so you can see at a glance which model packs your content most efficiently before you commit to it.
How it works
Exact counts need each vendor’s full tokenizer vocabulary, which is too large to ship to the browser. Instead, this tool uses calibrated heuristics per family: it counts words, then adds tokens for punctuation, runs of digits, whitespace patterns, and non-Latin characters, applying family-specific factors that mirror how each tokenizer tends to split text. The result is a close estimate plus a characters-per-token ratio for each model, where higher means more efficient.
How to read it
- GPT (cl100k/o200k-style) is efficient on English prose and code.
- Claude tends to be in a similar range with its own splitting behaviour.
- Llama (SentencePiece) often produces more tokens on punctuation-heavy or non-English text.
- Gemini-style counts are estimated similarly for comparison.
Use the characters-per-token column to pick the most efficient model for your specific content — code, prose, and multilingual text rank differently.
When the gap between tokenizers matters most
For most straightforward English text, the difference between tokenizer families is modest — you might see a 10–15% variation in token count. That difference becomes significant in several cases:
Code-heavy prompts. Programming languages use punctuation densely (brackets, semicolons, operators). SentencePiece-based tokenizers can produce noticeably more tokens for the same code block than BPE models tuned on code-heavy corpora. If your application embeds code snippets in every request, this can materially change your cost.
Structured data. JSON, XML, and similar formats with repetitive keys and punctuation tokenize differently across families. Compact serialization (no extra whitespace) helps all tokenizers but helps some more than others.
Non-Latin scripts. The divergence between tokenizer families is largest for languages not heavily represented in English-focused training. For CJK, Arabic, or Cyrillic text, the characters-per-token ratio can vary significantly between GPT and Llama families.
Mixed-language content. Documents that switch between English and another language produce highly variable token counts. Test a representative multilingual sample rather than extrapolating from pure-English efficiency.
Using this to plan context budgets
If your use case involves a fixed context window — for example, retrieval-augmented generation where you need to fit a set number of retrieved documents — token count per document matters directly. Running candidate documents through the playground for your target model helps you estimate how many can fit before truncation occurs.
Tips and notes
- Test your real content. Efficiency depends heavily on language, code, and formatting — measure a representative sample, not a single word.
- Verify before billing. For exact figures, run your text through the vendor’s official tokenizer; this is for fast comparison.
- Watch non-English text. Many tokenizers are far less efficient on non-Latin scripts, which can multiply your token cost.
- The characters-per-token ratio is your headline number. A ratio of 4.0 means roughly four characters fit per token; 3.0 means the model is less efficient for this content and you should budget more tokens accordingly.