Estimate how many tokens your text will use across GPT and Claude models before sending it to an API.
Large language models don't process text as individual characters or whole words, they break text into "tokens," chunks that are often subword fragments. The word "tokenization" might split into "token" + "ization" as two tokens, while common short words like "the" or "and" are usually a single token each. As a rough rule of thumb for English text, 1 token is approximately 4 characters, or about 0.75 words, meaning 100 tokens is roughly 75 words of typical English prose.
Every API call to a language model has two hard constraints tied to tokens: the total context window (the maximum combined input + output tokens a model can process in one conversation) and cost (nearly every commercial LLM API charges per token, separately for input and output). Understanding roughly how many tokens your prompt or document consumes lets you estimate costs before calling an API and avoid hitting context window limits mid-conversation.
Tokenizers are trained primarily on English-dominant text corpora, which means English text tokenizes efficiently (close to the ~4 characters per token rule), while other languages, especially those with different scripts like Hindi, Arabic, or Chinese, often require significantly more tokens per character or per word to represent the same content. This has real cost implications for multilingual applications, the same sentence can cost meaningfully more in tokens (and therefore API cost) in some languages than in English.
This tool uses a pattern-based approximation that splits text on word boundaries, numeric sequences, and punctuation clusters, closely mirroring how real Byte-Pair Encoding (BPE) tokenizers used by GPT models behave. It typically lands within 5% of the exact count for standard English prose. For production applications where exact token counts matter (billing reconciliation, precise context window management), use the model provider's official tokenizer library (like OpenAI's tiktoken) directly in your code.