Estimate how many tokens your text uses for GPT-4, Claude, Gemini, LLaMA, and more. Calculate API costs and check context window usage.
GPT-4 / GPT-4o
Context window: 128.0K tokens • Tokenizer: cl100k_base • ~4 chars/token
Token counts are estimates based on character ratios. Actual token counts may vary slightly due to different tokenization algorithms. For precise counts, use official tokenizer libraries like tiktoken (OpenAI) or the respective model's tokenizer.
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Large language models (LLMs) like GPT-4, Claude, and Gemini process text as tokens—subword units that affect API pricing and context limits. Our calculator estimates token counts across popular models, helping you optimize prompts and predict costs.
Tokens are the fundamental units that LLMs use to process text. A token can be a word, part of a word, or even punctuation. English text averages about 4 characters per token, meaning 'tokenization' might split into 'token' and 'ization'. Different models use different tokenizers (BPE, SentencePiece), affecting exact counts.
Token Estimation Formula
A developer counts the tokens in a prompt to make sure it fits within a model's input limit
A user checks whether a long article can be sent to an AI model without being truncated
Someone verifies how many tokens remain for the model's reply after the prompt is counted
An engineer compares how the same text tokenizes across different models to plan usage
LLM APIs charge per token. GPT-4 costs ~$0.01 per 1K input tokens. Knowing your token count helps budget API usage and avoid unexpected costs.
Each model has a maximum context window (GPT-4: 128K, Claude 3: 200K, Gemini: 1M tokens). Exceeding this limit truncates your input or causes errors.
Shorter prompts cost less and often perform better. Token counting helps identify verbose sections to trim without losing meaning.
Output tokens also count toward limits and costs. Reserve space in your context window for model responses.
Each model uses proprietary tokenizers with different vocabularies. GPT-4 uses cl100k_base, Claude uses its own BPE tokenizer. Our estimation uses character ratios that are accurate within 5-10% for English text. For exact counts, use official libraries like OpenAI's tiktoken.
Yes, significantly. Tokenizers are trained primarily on English, so other languages are less efficiently encoded. Chinese, Japanese, and Korean may use 1.5-2x more tokens. Some languages like Shan can use up to 15x more tokens for the same meaning.
Context window is the total capacity for input AND output combined. If you use 100K tokens of input with a 128K context window, only 28K tokens remain for the response. Plan your prompts to leave room for adequate responses.
Code often tokenizes less efficiently than prose. Keywords, variable names, and syntax all become separate tokens. A single line of code might use 20+ tokens. Minified code typically uses fewer tokens than formatted code.
Model size, capability, and operational costs determine pricing. GPT-4 is more expensive than GPT-3.5 due to its larger parameter count and better reasoning. Open source models like LLaMA have no API costs but require infrastructure to run.