What is tokenization?

Tokenization is the process of splitting text into smaller units called tokens that language models can process. Different models use different tokenization strategies. For example, 'dog' might be one token, but 'unbelievable' might be split into 3 tokens.

Why do different models tokenize differently?

Different models are trained with different vocabularies and tokenization algorithms. BERT uses WordPiece, GPT models use Byte-Pair Encoding (BPE), and other models use SentencePiece. This affects context usage and token counts.

How does tokenization affect my prompts?

Tokenization affects how much context you can use (context window), how much an API call costs (usually priced per token), and how a model understands your text. Unusual token splits can sometimes confuse models or waste tokens.

What are problematic tokens?

Problematic tokens are unusual splits where a word is broken into many fragments, or where special characters create unexpected boundaries. These can waste tokens and sometimes affect model understanding.

BERT/GPT Tokenizer Visualizer

Paste your text and see how different tokenizers (BERT, GPT-4, Claude, etc.) break it into tokens. Understand token counts and identify problematic splits.

Select Tokenizer:

GPT-4 / GPT-4 Turbo

cl100k_base

128K vocab

Text to Tokenize:

Total Tokens

Avg Tokens per Word

0.0

Character Count

Problematic Tokens

Token Visualization:

Token Details:

Estimated API Cost:

How to Use

Select a tokenizer – Choose BERT, GPT-4, GPT-2, or Claude
Paste your text – Enter text you want to analyze
Click Visualize – See tokens, IDs, and highlighting
Review stats – Check token count, efficiency, and costs

Understanding Token Colors

Normal Token – Standard word or subword token
Special Token – [CLS], [SEP], [UNK], start/end markers
Problematic – Rare or unusual splits that waste tokens

Tokenization Algorithms

BPE (Byte-Pair Encoding): Used by GPT-2, GPT-3.5, GPT-4, RoBERTa. Splits words into subwords based on frequency. Good for handling rare words and multiple languages.
WordPiece: Used by BERT and related models. Similar to BPE but uses likelihood instead of frequency. Uses ## prefix for subword continuations. Prefers longer tokens.
SentencePiece: Used by Llama, T5, XLNet, PaLM/Gemini. Treats spaces as tokens (▁). Handles multiple languages well. Creates more linguistically meaningful splits.
cl100k_base & p50k_base: OpenAI-specific encodings. cl100k_base is newer (128K vocab) and more efficient than p50k_base (50K vocab).
Custom (Anthropic): Claude models use a custom tokenizer optimized for instruction-following and long context.

Example: Token Count Comparison

Text: "Hello, I'm learning about tokenization!"

GPT-4 (cl100k_base)

8 tokens

Hello | , | I | ' | m | learning | about | tokenization | !

BERT (WordPiece)

12 tokens

Hello | , | I | ' | m | learn | ##ing | about | token | ##ization | !

Llama 2 (SentencePiece)

9 tokens

Hello | , | I | ' | m | learning | about | tokenization | !

Key insights:
• GPT-4 is most efficient (8 tokens) with cl100k_base
• BERT splits more aggressively due to smaller vocab (12 tokens)
• Llama 2 balances between efficiency and linguistic meaning (9 tokens)

Why Token Count Matters

Context Window: You can only use as many tokens as the model's context window. Longer prompts consume tokens faster.
API Costs: Most LLM APIs charge per token. Fewer tokens = lower costs.
Model Understanding: Unusual token splits can sometimes confuse models or reduce understanding.
Performance: More tokens means slower processing. Optimizing tokenization improves speed.

Related Tools

Token Counter – Quick token counting for any text
Prompt Cost Calculator – Estimate API costs for different models
Context Window Calculator – Check remaining context and prevent overflow
Prompt Template Generator – Create optimized prompts
Temperature & Top-K Explainer – Understand sampling parameters

Tokenizer Visualizer