Learn about language model tokenization with OpenAI's flagship models.

One way to reduce the amount of unknown tokens is to go one level deeper, using a character-based tokenizer. Character-based tokenizers split the text into characters, rather than words. This has two …

Large Language Models (LLMs) such as GPT-4, Llama 3.1, and Claude Sonnet 3.5 have revolutionized natural language processing (NLP). Central to these models is the tokenizer, a crucial component …

Aug 4, 2025 · The tokenizer is the LLM component that splits text into tokens and makes them digestible by the model. Different LLMs use different tokenizers and not all of them split text the same way.

Use this free LLM token counter to quickly estimate GPT, Llama, and Gemini prompt size, track token usage, and understand API costs. Our privacy-first calculator works entirely in your browser.

Sep 11, 2025 · In this blog, we will break down everything related to LLM tokenization, starting with what it is, why it matters, the algorithms behind it, LLM tokenization techniques, common problems, and …

Andrej Karpathy recently published a new lecture on large language model (LLM) tokenization. Tokenization is a key part of training LLMs but it's a process that involves training tokenizers using …

Sep 3, 2025 · Tokenization is crucial for LLM performance, affecting vocabulary size, processing efficiency, and fairness across languages. Modern methods include adaptive, dynamic, and tokenizer …

Tokenization plays an essential role in shaping the quality and diversity of generated text, by influencing the meaning and context of the tokens in LLMs. In addition to text segmentation, it optimizes …

May 14, 2025 · Tokenization is the critical first step in the processing pipeline of Large Language Models (LLMs). It transforms raw text into numerical representations that models can understand and …