🔢 Token Calculator

Every large language model breaks text into tokens — small chunks of characters, sub-words, or whole words — before it can process a prompt or generate a reply. This token calculator lets you paste any piece of text and instantly see how many tokens it would consume across four major tokenizer families: GPT/OpenAI, Claude/Anthropic, Gemini/Google, and the open-source SentencePiece/BPE tokenizers used by Llama and Mistral. Unlike a cost calculator, this tool deliberately stays focused on the token count itself — no dollar figures — so you can quickly answer questions like "how many tokens is this document?" or "which tokenizer is more efficient for my content?" before you ever touch pricing.

How It Works

As you type or paste text into the box, the calculator counts characters and words instantly, then applies a characters-per-token ratio for each tokenizer family — about 4 characters per token for GPT and Gemini, 3.9 for Claude, and 4.7 for Llama/Mistral open-source tokenizers — to produce four parallel token estimates. A language multiplier adjusts all four estimates together, since non-Latin scripts like Japanese, Chinese, Korean, Arabic, and Hindi are typically split into more, smaller tokens per character than English, Spanish, French, or German text. The bar chart and comparison list update live so you can see how token counts shift as you edit your text or change language.

Why It Matters

Token count drives two things regardless of which model you use: how much of the context window your prompt consumes, and — once you bring in a pricing sheet — how much it costs to run. Knowing the approximate token count of a document, prompt template, or chat history helps you gauge whether it will fit inside a model's context window, whether you need to summarize or chunk it first, and how it compares across providers before you commit to one. This is especially useful when migrating a prompt between providers, since the same English paragraph can tokenize to a noticeably different count on GPT versus Claude versus an open-source Llama tokenizer.

Tips for Accurate Results

• Treat every number here as an estimate — the only fully accurate token count comes from the provider's official tokenizer, such as OpenAI's tiktoken library or Anthropic's token counting endpoint.
• Select the language multiplier that best matches your primary text — mixing scripts (e.g. English with embedded Chinese) will make the estimate less precise.
• Whitespace, punctuation, and formatting (Markdown, JSON, code) can all shift real tokenizer output; code and structured data often tokenize less efficiently than plain prose.
• If you need exact costs rather than just counts, feed the token numbers from this page into NeftCal's LLM API Cost Calculator or Prompt Cost Estimator.
• Very short text samples (a single sentence) can show larger relative differences between tokenizer families than longer documents, where the ratios tend to average out.