About this tool
AI Model prompt engineering requires careful token management to optimize API call expenses and bypass hard context length boundaries.
Prompt Tokenization Formulations
Tokenizer engines process text characters into integer matrices before feeding them into deep neural layers. For basic English blocks, token lengths can be projected using standard character ratios:
Where:
- T is the projected token count.
- C is the total character length of your text prompt.
Using this tracker, you can estimate pricing across OpenAI, Anthropic, and Gemini models before running live API handshakes.
Frequently asked questions
Everything you need to know about AI Token Counter & Cost Estimator.
Which AI models does the token counter support?
The counter supports OpenAI models (GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo), Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus), and Google Gemini models. Each uses a different tokenisation algorithm, so the same text may produce different token counts across models.
How is a token different from a word?
Tokens are subword units, not whole words. A common word like 'cat' is typically one token, but 'tokenisation' might be split into two or three tokens. On average, 100 tokens ≈ 75 English words. Languages with longer words (German, Finnish) or non-Latin scripts (Chinese, Arabic) have different token-to-word ratios.
Why does the same text produce different token counts on different models?
Each model uses its own tokeniser (vocabulary). GPT models use Byte Pair Encoding (BPE) via the tiktoken library. Claude uses a different vocabulary. Gemini uses SentencePiece. Because the vocabularies differ in size and composition, token boundaries fall in different places for the same input text.
How does the tool estimate API costs?
Cost = (Input tokens ÷ 1,000) × input price per 1K tokens + (output tokens ÷ 1,000) × output price per 1K tokens. The tool uses the current published pricing from each provider. Prices are updated regularly but may lag behind provider announcements by days.
How accurate is the token count compared to the actual API?
For GPT models, the count is exact — it uses the same tiktoken library OpenAI uses internally. For Claude and Gemini, the count is a close approximation using their published tokenisation documentation. Counts may differ by 1–3% from the actual API billing count.