How token counter works
Large language models (LLMs) process text in chunks called tokens. A token is roughly 4 characters or 0.75 words of English text. This counter estimates how many tokens your input will use across different models, helping you stay within context windows.
Different models have different context window sizes: GPT-4o supports 128K tokens, Claude 3.5 supports 200K, and older models like GPT-4 have 8K. The counter shows your estimated token count, the context window for your chosen model, what percentage of the window you are using, and how many tokens remain.
The estimate uses the standard 4-characters-per-token heuristic, which is accurate for English text but may vary for other languages or code. For exact counts, use the official tokenizer libraries. This tool is perfect for quick planning and prompt engineering.
Frequently asked questions
How are tokens counted?
Tokens are estimated using the standard heuristic of approximately 4 characters per token for English text. The actual count may vary slightly depending on the model tokenizer, but this is accurate enough for planning and prompt engineering.
What is a context window?
A context window is the maximum number of tokens a model can process in a single request. GPT-4o has 128K tokens, Claude 3.5 has 200K, and older GPT-4 has 8K. Exceeding it causes errors.
How accurate is this counter?
The estimate is based on the 4-char-per-token average, which is accurate for English text. For exact counts, use OpenAI tiktoken or Anthropic tokenizer libraries. This tool is for quick planning and budgeting.
How can I reduce my token count?
Use shorter, more concise text. Remove unnecessary words, use abbreviations, and split long documents into chunks. Each token costs money on API calls, so reducing tokens saves cost.
