Tokenization

Tokenization is the process of splitting raw text into discrete units called tokens that a language model can process, the foundational preprocessing step for every text-based AI system. Tokens are typically subword units — pieces of words rather than whole words or characters — chosen by algorithms like Byte-Pair Encoding, WordPiece, or SentencePiece to balance vocabulary size against sequence length. A given English sentence typically becomes about 30% more tokens than words, while non-Latin scripts like Chinese, Japanese, and Korean often produce far more tokens per character. Tokenization choices have enormous downstream effects: vocabulary size affects model parameter count, tokenization granularity affects effective context length, and the chosen algorithm affects multilingual fairness. Each model family uses its own tokenizer — OpenAI uses tiktoken (cl100k_base, o200k_base), Anthropic Claude uses a proprietary tokenizer, Google Gemini uses SentencePiece, and Llama uses a custom BPE — so token counts and costs differ between providers even for identical input text. AI governance teams document the tokenizer used in every model deployment for AI compliance traceability and accurate cost forecasting.

Tokenization governance through Centralpoint: Centralpoint accounts for per-model tokenization across every LLM in its stack — OpenAI, Anthropic, Gemini, Llama, embedded models — so token metering and cost forecasts stay accurate. The model-agnostic platform keeps prompts local and deploys tokenizer-aware chatbots through one line of JavaScript with audit-ready governance.


Related Keywords:
Tokenization,,