Token Counter for LLMs

Estimate token counts and costs for GPT-4, Claude, Gemini, Llama and more. Runs entirely in your browser — nothing is sent to any server.

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The transformer architecture introduced in "Attention Is All You Need" (Vaswani et al., 2017) fundamentally changed natural language processing. By replacing recurrent networks with self-attention mechanisms, transformers can process sequences in parallel and capture long-range dependencies more effectively.

Large language models like GPT-4 and Claude are trained on massive corpora of text using next-token prediction. The model learns to assign probabilities to sequences of tokens, enabling coherent text generation through sampling or greedy decoding.

Tokenization is the process of splitting raw text into discrete units called tokens. Most modern LLMs use byte-pair encoding (BPE) or a similar subword algorithm. A single token is roughly 4 characters of English text on average, though code and non-Latin scripts tokenize differently.

Understanding token counts matters for cost estimation, context window planning, and prompt optimization. This tool provides fast in-browser estimates without sending your text to any server.

~260

tokens (estimated)

~ prefix indicates approximation via BPE-like char-based estimation

~260

Tokens

1,038

Chars

144

Words

Sentences

Model

GPT-4o

Context window: 128,000 tokens

~$0.0013

estimated input cost

Context window usage0%

260 / 128,000 tokens used

Input price

$5 / 1M tokens

Output price

$15 / 1M tokens

All Models Comparison

Model

Tokens

Context %

Input cost