Paste text below — see estimated tokens for every major AI model
Estimates may vary ±10% from actual tokenization
Tokens are the fundamental units that large language models use to process text. A token can be an entire word, a subword, or even a single character. In English, one token averages about 3–4 characters. AI providers charge based on token count, so understanding your usage helps control costs.
Each AI model uses its own tokenizer algorithm (like BPE or SentencePiece) to split text into tokens. This tool uses character-based heuristics calibrated to each model’s typical compression ratio. For precise counts, use the model provider’s official tokenizer library.
Different models are trained with different vocabulary sizes and tokenization strategies. Claude uses a different tokenizer than GPT-4, which splits text differently. A larger vocabulary generally means fewer tokens for the same text, but the relationship is complex and depends on the training data.
Pricing varies widely by model. GPT-4o charges ~$2.50 per million input tokens, Claude Sonnet charges ~$3 per million input tokens, and the older GPT-4 charges ~$30 per million input tokens. Output tokens typically cost 2–4x more. Open-source models like Llama are free to run on your own hardware.
Token count directly affects your API costs and determines whether your prompt fits within a model’s context window. GPT-4o supports 128K tokens, Claude supports 200K tokens, and Llama 3 supports up to 128K tokens. Staying within limits avoids truncated responses and optimizes spending.