Tokenizer Visualizer — See How LLMs Split Text Into Tokens

The Tokenizer Visualizer provides a real-time, client-side look into how large language models (LLMs) break down human text. When you paste text, the tool does not send it to an external API; instead, it runs the official gpt-tokenizer library entirely in your browser.

You will see your input split into individual colored chips—these are tokens. The visualization allows you to instantly compare how different models, like GPT-4 (cl100k_base) and GPT-4o (o200k_base), handle the same text. It provides live statistics showing token count, character count, and estimated word count, alongside a direct calculation of potential API costs based on current pricing structures.

This side-by-side comparison is crucial because models use different underlying encoding schemes, meaning the same sentence can yield slightly different token counts depending on which tokenizer is applied.

Use the Tokenizer Visualizer proactively to optimize your prompts before they hit the API. Think of tokenization as a form of data compression.

• Be Concise: Instead of pasting large blocks of raw, unstructured data, summarize or structure it into bullet points where possible. This often reduces token count without losing critical information.
• Choose the Right Model: If your content is highly conversational and requires rapid iteration, comparing models like GPT-4o against older versions can help you select the most cost-effective tokenizer for your specific task type.
• Test Edge Cases: Paste text containing special characters (emojis, mathematical symbols, non-Latin scripts) to see exactly how the model handles them, as these often consume more tokens than expected.

By treating tokenization as a primary design constraint, you ensure efficiency and predictability in your AI workflows.