🤖 AI Tool

AI Prompt Length
Checker

Estimate your prompt's token count for ChatGPT, Claude and Gemini. Free, instant, no signup.

🤖 AI Prompt Length Checker

Token estimate uses the common ~4 characters-per-token approximation — actual tokenization varies by model.

📏 Character Limits Reference

FieldLimitNotes
ChatGPT (free, GPT-4o mini)~8,000-16,000 tokens contextVaries by tier and model version
ChatGPT Plus (GPT-4o/o-series)~32,000-128,000 tokens contextDepends on selected model
Claude (web/app, standard)~200,000 tokens contextOne of the largest available context windows
Gemini (Advanced)~1,000,000+ tokens contextLargest publicly available context window as of 2026
Rough token estimate~4 characters per tokenEnglish text approximation; varies by language and content
Rough word-to-token ratio~0.75 words per token1,000 words ≈ 1,300 tokens (approximate)

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Frequently Asked Questions

Common questions about character limits and how the tool works.

A token is the basic unit an AI language model processes text in — roughly a word, part of a word, or punctuation mark, rather than a single character. As a rough approximation for English text, one token is about 4 characters or about 0.75 words.
ChatGPT's limit depends on the model and context window rather than a fixed character count. Free-tier models typically support a context window in the thousands of tokens, while paid tiers with newer models can support context windows of 32,000 to 128,000+ tokens, equivalent to roughly 100,000-500,000 characters.
Claude's standard context window is approximately 200,000 tokens, equivalent to roughly 150,000 words or 800,000 characters — one of the largest context windows publicly available as of 2026.
A common rule of thumb is 4 characters per token or about 0.75 words per token for English text. So a 1,000-word prompt is roughly 1,300 tokens. This is an approximation — actual tokenization varies by model and language.
Yes, completely free with no signup. It estimates your prompt's token count using character-based approximation, useful for gauging whether a prompt fits comfortably within a given model's context window.