AI Token Counter — Count Tokens & Estimate API Cost for GPT, Claude, Gemini [2026]

Tokens are chunks of text that AI models process. In English, 1 token is roughly 4 characters or about 0.75 words. The word "artificial" is 2–3 tokens, while "AI" is 1 token. Models use tokenizers (like BPE) to split text into these chunks before processing.

Each model family uses a different tokenizer — the algorithm that splits text into tokens. OpenAI GPT models use cl100k_base or o200k_base, Anthropic Claude uses its own tokenizer, and Google Gemini uses SentencePiece. The same text may produce slightly different token counts across models.

Generating text (output) requires sequential computation — the model predicts one token at a time, each depending on the previous one. Processing input tokens can be parallelized. This makes output generation 2–5x more compute-intensive, which is reflected in higher pricing.

For English text, estimates are typically within 5–10% of actual counts. The accuracy may vary for non-English languages, code, or text with many special characters. For exact counts, use each provider's official tokenizer. This tool is designed for quick estimation and cost planning.

The context window is the maximum number of tokens a model can process in a single request, including both your input (prompt) and the model's output (response). For example, GPT-4o supports 128K tokens, Claude 3.5 Sonnet supports 200K tokens, and Gemini 1.5 Pro supports up to 2M tokens.

Keep prompts concise and remove unnecessary context. Use system prompts efficiently. Route simple tasks to cheaper models (like GPT-4o Mini or Claude Haiku). Set max_tokens limits on responses. Use prompt caching for repeated context. Consider batch APIs for non-real-time tasks, which typically offer 50% discounts.