Client-Side LLM Tokenizer Demo

Interactive tokenizer explorer for GPT-style tokenization concepts (approximation demo).

Token output

How GPT tokenization works (short version)

Educational

• GPT models use a tokenizer (byte-level BPE family) to convert text into token IDs.
• A token may be a whole word, a word piece, punctuation, or whitespace fragment.
• Common text patterns become single tokens; rare patterns split into multiple tokens.

The math

• text → tokens → ids
• Embedding: x_t = E[id_t] (lookup vector for each token id)
• Attention and MLP layers process these vectors to predict the next token distribution.
• Cost is token-based: total_cost ∝ input_tokens + output_tokens

Useful rule of thumb

For English, ~1 token ≈ 3–4 characters on average, but code, emojis, and mixed-language text can blow this up.

Important accuracy note

This page is a client-side educational approximation, not OpenAI’s exact tokenizer tables. Use it to understand behavior and estimate costs, not for exact billing.