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VePrompts

Free LLM Tokenizer

Count tokens for GPT-4, GPT-4o, Claude, Gemini, Llama, and DeepSeek. Visualize token IDs, compare models, and share results with a URL.

Last updated: 2026-06-12

Enter text above to see token counts and comparisons.

How does LLM tokenization work?

Tokens are the unit of language models

Large language models do not read characters or words directly. They process tokens — common sequences of characters that the model learns during training. A token can be a whole word, part of a word, or even a single punctuation mark. English averages roughly 0.75 words per token.

Why token count matters

API pricing, context window limits, and rate limits are all measured in tokens. If you send a 4,000-token prompt to a model with a 4,096-token context window, you only leave room for a 96-token response. Knowing your token count helps you budget costs and fit inputs within model limits.

Exact vs. approximate counts

OpenAI publishes its tokenizer encoders, so counts for GPT-4, GPT-4o, and GPT-3.5 are exact. Anthropic, Google, Meta, and DeepSeek do not publish official tokenizers. For those models we use a widely-accepted BPE encoder as a close approximation and clearly label the result.

Share your token counts

The URL updates automatically as you type and switch models. Copy the address bar to share an exact snippet with teammates, or save a bookmark for a prompt you tokenize often. Your text never leaves your browser.

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