Interactive AI Token Calculator
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Launch AI Token Calculator1. The Tokenization Pipeline
The text pipeline goes from Raw Text → Tokenization (Splitting) → Vocabulary Mapping (Tokens to IDs) → Embedding Projection (IDs to high-dimensional vectors). These vectors represent the mathematical weights that the neural network's transformer blocks can process.
2. Tokenization Algorithms: BPE vs. WordPiece vs. SentencePiece
Different models use different tokenizer types. BPE (Byte-Pair Encoding) merges characters based on frequency (GPT, Llama). WordPiece selects merges that maximize the likelihood of the training data (BERT). SentencePiece treats input as a raw byte stream and doesn't require pre-tokenization spaces, making it ideal for multilingual models.
3. Loss of Context in Bad Tokenization
If a tokenizer splits words poorly, it degrades model comprehension. For example, if the tokenizer splits a technical term into nonsense fragments, the model must work harder to associate those fragments with the true underlying concept, leading to lower recall.
Frequently Asked Questions
What is a vocabulary size in tokenization?
It is the total number of unique tokens the tokenizer knows. For example, GPT-4's tokenizer has a vocabulary of ~100k, while Llama 3's is ~128k.
Why does tokenization happen on the client or server?
Tokenization must happen before the model runs. In APIs, it happens on the provider's server, but developers tokenize locally (using tiktoken) to calculate costs before sending the request.