Are these exact tokenizer counts?

No. True token counts require each model's exact tokenizer (tiktoken for GPT, the Claude tokenizer, SentencePiece for Llama). This playground uses calibrated heuristics that account for whitespace, punctuation, digits, and non-Latin scripts to approximate each family closely without bundling megabytes of vocab.

Why do models tokenize the same text differently?

Each model is trained with its own byte-pair or sub-word vocabulary, so the same word can split into a different number of tokens. Code, rare words, emoji, and non-English scripts show the biggest divergence between families.

Why does characters-per-token matter?

A higher characters-per-token ratio means the model packs more of your text into each token, so the same content costs fewer tokens. For long documents or high volume, that efficiency difference translates directly into lower cost and more usable context.

Is my text sent anywhere?

No. All tokenization estimates run locally in your browser. Nothing you paste is uploaded, stored, or logged.

Multi-Model Tokenizer Playground

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.

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.