About AI Token Counter

AI Token Counter is a free online tool that helps you estimate how many tokens your text will consume when sent to popular Large Language Models (LLMs) like GPT-4, Claude, Gemini, and Llama. Understanding token usage is crucial for prompt engineering, API cost estimation, and staying within model context limits. This tool runs entirely in your browser — no data is sent to any server.

Features

Support for 5 major LLM families: OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama
Real-time character, word, and token count estimation as you type
Model-specific context window display (128K, 200K, 1M, etc.)
Visual usage bar with color-coded safety indicators (safe/warning/danger)
Remaining token count to help plan prompt length
One-click copy and clear for quick workflow
Calibrated estimation using character and word-based algorithms
100% client-side — zero data transmission to servers
Works with code, markdown, JSON, XML, and any plain text
Free, unlimited usage with no registration required

How to Use

Select your target LLM from the dropdown (GPT-4, Claude, Gemini, etc.).
Paste or type your prompt, text, or document into the textarea.
Watch the stats update in real time: characters, words, and estimated tokens.
Check the usage bar to see what percentage of the context window you are using.
If approaching the limit (yellow), consider shortening your prompt.
Use the Copy button to grab your text, or Clear to start fresh.

Common Use Cases

Developers estimating API costs before sending large prompts to OpenAI or Anthropic
Prompt engineers optimizing context usage to fit within model limits
Content creators planning long-form content for AI-assisted writing workflows
Researchers checking if their document fits within a model's context window for summarization
Students verifying token counts for assignments using AI tools with strict limits
Teams budgeting AI usage by estimating tokens per request across different models

Frequently Asked Questions

How accurate are the token estimates?

Our estimates use a hybrid approach combining character-based and word-based calculations calibrated per model family. They are typically within 10-20% of actual tokenizer counts. For exact numbers, use the official tokenizer (e.g., tiktoken for OpenAI models).

Is my text data sent to any server?

No. All processing happens entirely within your browser. We do not store, transmit, or log any of your input text.

What is a token in LLM context?

A token is a unit of text that LLMs process. It can be as short as one character or as long as one word. For English text, 1 token is roughly 0.75 words or 4 characters. Different models use different tokenization algorithms.

Why do different models show different token counts?

Different LLM families use different tokenization algorithms. For example, GPT-4 uses BPE (Byte Pair Encoding), while Claude uses a different tokenizer. This tool calibrates estimates per model family for better accuracy.

What happens if I exceed the context window?

If your text exceeds the model's context window, the API will typically truncate the input or return an error. Our danger indicator (red) warns you when you are over the limit so you can shorten your prompt.

Does this tool support non-English text?

Yes, but token counts for non-English text (especially Chinese, Japanese, Arabic) are often higher per character than English. Our estimates are calibrated for English-like text and may be less accurate for other languages.

Can I use this for code and structured data?

Yes. The tool works with any plain text including code (Python, JavaScript, etc.), JSON, XML, Markdown, and CSV. Code often tokenizes differently than prose due to punctuation and whitespace patterns.

How do I get exact token counts for OpenAI models?

For exact counts, use OpenAI's tiktoken library (Python/JS) or the OpenAI Tokenizer web tool. Our counter provides quick estimates for planning purposes without installing anything.

AI Token计数器

估算方法