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AI Token Counter
An AI token counter measures how a specific model breaks an input into tokens. There is no universal token total for every AI model: the same text can produce a different count when the provider, model, tokenizer, or request structure changes. Select the model first, then count the complete input you intend to send.
What an AI token counter measures
AI models process tokens rather than raw words. A tokenizer can represent a short word as one token, split a longer or less common word into several tokens, and treat punctuation, code, whitespace, or non-English text differently. That is why a word counter cannot replace a model-aware token counter.
Why the selected model changes the answer
Providers use different tokenization systems, and tokenizer versions can change between model generations. A useful count therefore identifies the provider and model or encoding. Reusing a count from another model can misstate how much of the context window the request occupies.
Count the complete request
Visible prompt text may be only part of the input. Depending on the API, the request can also contain a system instruction, earlier conversation turns, tool definitions, images, audio, PDFs, or other structured content. Provider counting endpoints can account for supported structured inputs more reliably than a plain text box.
Use the count correctly
An input-token count helps check whether a request fits the model’s context window and provides an input for cost planning. It does not predict the final output-token count, because the output does not exist until the model generates it. After obtaining the appropriate count, use the Token Cost Kit calculator for token-based cost planning. For OpenAI text encodings, use the OpenAI Token Counter.
Provider references
- Anthropic token counting describes counting structured Claude messages before sending them.
- Google’s Gemini token guide explains input and output token counting across supported modalities.