LLM Token Counter

What is a token in the context of LLMs?

A token is a chunk of text that language models process. Tokens can be words, parts of words, or even individual characters. For English text, one token is roughly 4 characters or about 0.75 words. Tokenization varies by model, but this tool provides accurate estimates for all major LLMs.

How accurate are the token estimates?

This tool uses character-based estimation ratios that closely approximate each model's actual tokenizer. For standard English text, estimates are typically within 5-10% of actual token counts. For code or non-English text, actual counts may vary more since different tokenizers handle these differently.

Why do different models have different token counts?

Each LLM provider uses a different tokenizer with its own vocabulary. OpenAI uses tiktoken, Anthropic uses their own tokenizer, and other providers have their own implementations. Different tokenizers split text into tokens differently, resulting in varying token counts for the same text.

How are API costs calculated?

API costs are calculated by multiplying the token count by the price per 1,000 tokens. Most providers charge separately for input tokens (your prompt) and output tokens (the model's response). This tool shows both estimates so you can budget your API usage accurately.

What is a context window and why does it matter?

A context window is the maximum number of tokens a model can process in a single request, including both input and output. For example, GPT-4 Turbo has a 128K token context window. If your input exceeds the context window, the API will return an error, so it's important to check your text fits within the limit.