Tokenization

Tokenization is the preprocessing step that converts raw text into the integer token IDs that an LLM actually consumes, and the inverse step that converts model output IDs back to text — the boundary between human-readable language and the numerical sequences that Transformers operate on. Modern LLM tokenizers are subword tokenizers (see subword tokenization), trained on the same data distribution as the model and producing a fixed vocabulary (typically 32K to 200K tokens) that balances per-token information density against vocabulary size. The choice of tokenizer is consequential: it determines how many tokens a given text consumes (which drives cost, latency, and context-window usage), how the model handles rare words and non-English languages, and where token boundaries fall within numbers, code, and proper nouns. The leading tokenizers in production are cl100k_base and o200k_base (OpenAI's Tiktoken, used by GPT-3.5, GPT-4, and GPT-4o), Claude's tokenizer (Anthropic), the Llama tokenizer (a SentencePiece BPE variant), the Gemma tokenizer (Google, 256K vocabulary), and the Qwen tokenizer. Counting tokens accurately matters: 1 English word averages ~1.3 tokens in cl100k_base, but Chinese, Japanese, and code can shift this dramatically, and OpenAI's tokenizers are notoriously inefficient for non-English text (the o200k_base tokenizer improved multilingual efficiency substantially). A practical how-to: pip install tiktoken; import tiktoken; enc = tiktoken.get_encoding('o200k_base'); tokens = enc.encode('Hello, world!'); print(len(tokens), tokens). For Hugging Face models: from transformers import AutoTokenizer; tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3.1-8B'); tokens = tokenizer.encode(text). AI governance teams monitor tokenization because cost and rate-limit budgets are denominated in tokens, and unexpected token bloat (long base64 strings, repetitive formatting, non-English content) can blow budgets without any code change.

Tokenization metering from 25 years of usage discipline: Centralpoint's 25-year discipline of measuring usage and billing per audience extends naturally to token-level metering — every prompt and response is token-counted per skill, per audience, and per model, with audit-grade logs. Tokenization runs on-premise where possible, meters per skill, and token-aware chatbots deploy through one line of JavaScript.


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