How to Count LLM Tokens and Estimate API Costs Locally

Large Language Models (LLMs) like OpenAI's GPT models, Anthropic's Claude, and Google's Gemini do not process text in characters or words. Instead, they break text down into chunks of characters called tokens. A token can be a single character, a syllable, a word, or even part of a word. Understanding the exact token count of your prompts is crucial since LLM providers charge based on token usage, and models have strict context window limits.

Different models use different tokenizer algorithms (or token encodings) to parse text. For example, GPT-3.5 and GPT-4 use the 'cl100k_base' encoding, while newer GPT-4o models use the 'o200k_base' encoding. Comparing your text across these encodings helps optimize prompts to fit within budgets and context thresholds.

Pastry templates, API prompts, and code blocks frequently contain sensitive data, including API keys, database credentials, proprietary code structures, or personal customer logs. Uploading this text to remote servers to count tokens exposes your information to compliance and security leaks. ZeroWebTools runs the tokenizer engine entirely inside your browser.

Our LLM Token Counter utilizes local Javascript libraries to load BPE (Byte Pair Encoding) rank arrays in-memory. The tokenization process, visual word-boundary rendering, and price estimations are processed on your local CPU. Your private prompts and text inputs never touch a remote server.