AI Prompt Token Counter (Approx)
Approximate token counts for GPT/Claude/Gemini-style tokenizers using a heuristic estimate.
Last reviewed by the Radiatus Cloud team
Output
Securing AI in production?
We build guardrails, governance & compliance for AI systems.
Estimate a prompt’s token count
Models measure and price text in tokens, not words or characters, and knowing a prompt’s token count matters for cost and context limits. This tool approximates token counts for GPT, Claude and Gemini-style tokenizers using a heuristic estimate.
Why tokens, and why approximate
A token is roughly a word-piece, and models count them to price requests and enforce context limits, so the token count of a prompt determines both what it costs and whether it fits. The exact count depends on the specific tokenizer, which differs between model families and is not something a simple tool reproduces precisely. A good heuristic, based on the general relationship between characters, words and tokens, gets close enough for budgeting and fit-checking. Treat the result as a solid estimate for planning, not the exact figure the model will bill, which only the model’s own tokenizer gives.
Close enough to plan
The tool runs entirely in your browser, so nothing you paste, prompts, outputs or documents, is uploaded, which matters when the input is sensitive AI data or your own content.
Related tools
- AI Prompt Leakage Analyzer — Paste a system prompt and a hostile user input to see whether the prompt holds secrets and whether the input carries injection patterns. Local, instant.
- LLM Data Exposure Checker — Check if text contains data likely to be memorized or exposed by LLMs.
- AI Usage Policy Generator — Generate an acceptable use policy for AI tools in your company.
- Model Hallucination Estimator — Estimate risk of hallucinations based on task type and temperature.
Frequently Asked Questions
Why do models count tokens instead of words?
Because tokens, roughly word-pieces, are how models process text. They are used to price requests and enforce context-window limits.
Why is the count only approximate?
Because the exact count depends on the specific tokenizer, which differs between model families. A heuristic gets close but not exact.
What is it good enough for?
Budgeting cost and checking whether a prompt fits a context window. For the exact billed count, only the model’s own tokenizer is authoritative.
Does it cover different models?
It approximates GPT, Claude and Gemini-style tokenizers, which share the rough relationship between characters, words and tokens.
Is my prompt uploaded?
No. The estimate runs entirely in your browser.
Privacy & Security
Processed locally in your browser. Accurate counts require model tokenizers.
How to Use
Paste text to get an approximate token estimate.
Disclaimer: This tool is provided "as is" without warranty of any kind. Results are for educational and utility purposes.
Related Tools
AI Prompt Leakage Analyzer
AI SecurityPaste a system prompt and a hostile user input to see whether the prompt holds secrets and whether the input carries injection patterns. Local, instant.
LLM Data Exposure Checker
AI SecurityCheck if text contains data likely to be memorized or exposed by LLMs.
AI Usage Policy Generator
AI SecurityGenerate an acceptable use policy for AI tools in your company.