AI Token Counter & Cost Calculator
Count tokens and estimate API costs for GPT-4o, Claude, Gemini, Llama, and Mistral. Paste your text and instantly compare pricing across all major AI models.
Enter some text above to see token estimates and cost breakdowns across all major AI models.
Frequently Asked Questions
What are tokens in AI language models?
Tokens are the basic units that AI language models use to process text. A token can be a word, part of a word, or even a single character. For English text, one token is roughly 4 characters or about 0.75 words. For example, the word 'hamburger' might be split into 'ham', 'bur', and 'ger' — three tokens. Understanding tokens is important because AI API pricing is based on the number of tokens processed.
How accurate are the token estimates?
The estimates use well-established heuristics: approximately 4 characters per token for GPT and Gemini models, and 3.5 characters per token for Claude models. These are accurate within about 10% for typical English text. Actual token counts can vary based on the specific content — code, non-English languages, and text with many special characters may tokenize differently. For exact counts, use the official tokenizer libraries like OpenAI's tiktoken.
Why do different AI models have different token counts for the same text?
Each AI model family uses its own tokenizer — the algorithm that splits text into tokens. OpenAI's GPT models use a BPE (Byte Pair Encoding) tokenizer, Anthropic's Claude uses a similar but distinct tokenizer, and Google's Gemini has its own as well. These tokenizers were trained on different datasets, so they split text into tokens differently. Claude's tokenizer tends to produce slightly more tokens for the same text compared to GPT.
What is the difference between input and output tokens?
Input tokens are the tokens in the text you send to the AI model (your prompt, context, instructions, etc.). Output tokens are the tokens in the response the model generates. Most AI providers charge different rates for input and output tokens, with output tokens typically costing 2-5x more than input tokens. This is because generating output requires more computational resources than processing input.
How can I reduce my AI API costs?
There are several strategies to reduce API costs: (1) Use shorter, more concise prompts to reduce input tokens. (2) Set a max_tokens limit on responses to control output length. (3) Use a cheaper model for simpler tasks — GPT-4o mini or Claude Haiku can handle many tasks at a fraction of the cost. (4) Cache common prompts and responses to avoid duplicate API calls. (5) Use batch APIs when processing many requests, as they often offer 50% discounts. (6) Consider open-source models like Llama for high-volume, cost-sensitive workloads.