AI Token Counter: Free Token Calculator for ChatGPT, Claude, Gemini, Copilot & Mistral

The first time I hit a token limit mid-conversation, I had no idea what happened.

I was deep into a coding session with GPT-4, pasting in a large codebase for review. The response just… stopped. Cut off mid-sentence. And I had no clue why, because I didn’t know what tokens were or that there was a limit.

If that sounds familiar, this tool is for you.

What Are Tokens?

Tokens are the fundamental units that AI language models use to process text. They’re not words—they’re chunks of text that the model’s tokenizer splits your input into.

A rough rule of thumb: 1 token is about 4 characters in English, or roughly 0.75 words. But it varies:

The tokenizer breaks text into pieces it learned during training. Common English words are often a single token. Rare words, code, and non-English text typically require more tokens per word.

Why Token Counts Matter

1. Context Window Limits

Every AI model has a maximum context window—the total number of tokens it can process in a single conversation (input + output combined):

If your prompt exceeds the limit, you’ll get truncated responses or errors.

2. API Cost Control

If you’re using AI APIs (not just the chat interface), you pay per token. The costs differ significantly between input and output:

A 1,000-token prompt to GPT-4o costs $0.0025 for input. DeepSeek V3 is the cheapest at $0.00028 input. The response always costs more than the prompt.

3. Prompt Optimization

Knowing your token count helps you:

• Trim fat from system prompts to save costs
• Estimate response budgets before API calls
• Stay within limits when pasting large documents
• Compare efficiency between different prompt approaches

How the Token Estimate Works

This tool uses the characters ÷ 4 heuristic, which is the standard approximation for English text. It’s accurate to within about 10% for typical content.

For exact counts, you’d need a model-specific tokenizer (OpenAI’s tiktoken, Anthropic’s tokenizer, etc.), since each model tokenizes slightly differently. But for estimation and cost planning, the ÷4 rule works well.

When the estimate is less accurate:

• Code and JSON (more tokens than expected)
• Non-English text (significantly more tokens)
• Text with lots of numbers or special characters
• Very short prompts (rounding has more impact)

Practical Tips for Token Management

For ChatGPT/Claude/Copilot Users (Chat Interface):

• You don’t pay per token on subscription plans, but context limits still apply
• Long conversations accumulate tokens—start fresh when things get slow
• Paste the most relevant context, not entire documents

For API Users:

• Set max_tokens on responses to control output costs
• Use cheaper models (GPT-4o mini, Haiku) for simple tasks
• Cache system prompts when possible
• Stream responses to stop early if the output isn’t useful

For Prompt Engineers:

• Shorter prompts aren’t always cheaper—a good system prompt saves money on retries
• Test with mini/flash models first, upgrade only when needed
• Use the cost table above to estimate before running batch jobs

Frequently Asked Questions

Is the token count exact? It’s an estimate based on the standard characters÷4 heuristic. For exact counts, you’d need model-specific tokenizers. The estimate is typically within 10% for English text.

Why do different models have different prices? Larger models with more parameters cost more to run. Pricing reflects compute requirements. Mini/flash models are cheaper because they’re smaller and faster.

What’s the difference between input and output tokens? Input tokens are what you send (your prompt). Output tokens are what the AI generates (its response). Output tokens typically cost 3-5x more because generation is more compute-intensive than reading.

Does this work for non-English text? The tool counts characters and estimates tokens. For non-English text, actual token counts will be higher than the estimate since non-Latin characters typically use 2-3 tokens each.

Do you store my text? No. Everything runs client-side in your browser. No text is sent to any server.