AI Token Counter
Count tokens for GPT-4, Claude, and other language models. Real-time character, word, and token counting.
About Token Counting
What are tokens? Tokens are pieces of words used by language models. A token can be as short as one character or as long as one word.
Why count tokens? Most AI APIs charge based on token usage. Knowing your token count helps estimate costs and stay within model limits.
Tokenization: This tool uses the GPT-4 tokenizer (cl100k_base). Different models may use slightly different tokenization.
Model Limits
Quick Tips
- • Shorter prompts = lower costs
- • 1 token ≈ 4 characters (English)
- • Code uses more tokens than prose
- • Special characters count as tokens
Complete Guide to AI Token Counting
What Are Tokens in AI?
Tokens are the fundamental building blocks that large language models (LLMs) like GPT-4, Claude 3, and Gemini use to process text. Unlike human readers who see words, AI models break down text into smaller pieces called tokens using a process called tokenization.
A token can be a whole word, part of a word (subword), a single character, or even punctuation. For example, the word "tokenization" might be split into "token" and "ization" – two separate tokens. Common words like "the" or "is" are typically single tokens, while rare or complex words get broken into multiple pieces.
This tokenization approach allows models to handle any text, including words they haven't seen before, by breaking them into familiar subword units. It's also why different languages have different token-to-character ratios – English averages 4 characters per token, while languages like Chinese may have 1-2 characters per token.
Why Token Counting Matters
Understanding token counts is essential for anyone working with AI APIs for three critical reasons:
- Cost Management: AI providers charge per token. GPT-4 Turbo costs $0.01/1K input tokens and $0.03/1K output tokens. Accurate counting helps budget projects.
- Context Limits: Each model has a maximum context window. Exceeding it causes errors or truncation. GPT-4 Turbo supports 128K tokens, Claude 3 Opus handles 200K.
- Prompt Optimization: Efficient prompts use fewer tokens while achieving the same results, directly reducing costs and improving response times.
Our AI Token Counter uses the official cl100k_base tokenizer (same as GPT-4), giving you exact counts for OpenAI models and accurate estimates for others.
2024-2025 LLM Context Windows & Pricing
| Model | Context Window | Input Price | Output Price | Best For |
|---|---|---|---|---|
| GPT-4 Turbo | 128K tokens | $0.01/1K | $0.03/1K | Complex reasoning, code |
| GPT-4o | 128K tokens | $0.005/1K | $0.015/1K | Multimodal, fast responses |
| Claude 3.5 Sonnet | 200K tokens | $0.003/1K | $0.015/1K | Long documents, coding |
| Claude 3 Opus | 200K tokens | $0.015/1K | $0.075/1K | Highest quality tasks |
| Gemini 1.5 Pro | 1M tokens | $0.00125/1K | $0.005/1K | Very long context tasks |
| GPT-3.5 Turbo | 16K tokens | $0.0005/1K | $0.0015/1K | Simple tasks, high volume |
How to Use This AI Token Counter
- Paste or type your text into the input area above. The tool accepts any text, code, or content you plan to send to an AI model.
- View real-time results as you type. You'll see character count, word count, and token count update instantly.
- Estimate your costs by multiplying the token count by your model's pricing. For example, 1,000 tokens to GPT-4 Turbo costs about $0.01 for input.
- Optimize if needed by shortening your prompt while maintaining meaning. Remove filler words, redundant instructions, and unnecessary context.
🔒 Privacy-First: Your Data Stays Local
Unlike many online tools, this token counter runs 100% in your browser. Your text is never transmitted to any server. All tokenization happens client-side using JavaScript. This makes it safe for sensitive content, proprietary code, and confidential documents. You can even use this tool offline once the page is loaded.
Related AI Development Tools
Frequently Asked Questions
What is a token in AI and large language models?
A token is the basic unit of text that AI language models process. Tokens can be whole words, parts of words, or even single characters. For example, the word "understanding" might be split into "under" and "standing" as two separate tokens. In English, one token roughly equals 4 characters or 0.75 words on average. Different languages and scripts may have different token-to-character ratios.
Why is token counting important for AI APIs?
Token counting is crucial because AI APIs like OpenAI, Anthropic, and Google charge based on token usage. Both input (prompt) and output (response) tokens are counted and billed. Additionally, each model has a maximum context window (e.g., GPT-4 Turbo has 128K tokens), and exceeding this limit will cause errors. Accurate token counting helps you estimate costs, stay within limits, and optimize prompts for efficiency.
How accurate is this token counter compared to OpenAI's tokenizer?
This tool uses the cl100k_base tokenizer, which is the same tokenization algorithm used by GPT-4, GPT-4 Turbo, and GPT-3.5 Turbo. For these models, the token count will be exact. For other models like Claude or Gemini, there may be slight differences (typically within 5%) as they use different tokenization schemes. For Claude, counts are usually very close since Anthropic uses a similar BPE tokenizer.
Does this tool store or send my text anywhere?
No. This is a 100% client-side, privacy-first tool. All tokenization happens directly in your browser using JavaScript. Your text is never sent to any server, stored in any database, or logged anywhere. You can verify this by checking the network tab in your browser's developer tools - no API calls are made when you paste text.
How do I reduce token usage and save costs?
To reduce token usage: 1) Be concise - remove unnecessary words and filler phrases. 2) Use abbreviations where context is clear. 3) Avoid repeating information in prompts. 4) Use system messages efficiently. 5) For code, remove comments and extra whitespace. 6) Consider using smaller models for simpler tasks. 7) Implement caching for repeated queries. Our LLM Cost Estimator tool can help you compare costs across different models.
What's the difference between input and output tokens?
Input tokens are the tokens in your prompt (what you send to the AI), while output tokens are the tokens in the AI's response. Most providers charge different rates for each - typically output tokens cost 2-4x more than input tokens. This tool counts input tokens. To estimate total cost, you'll need to also account for expected output length.
Can I use this for Claude, Gemini, or other AI models?
Yes! While this tool uses the GPT-4 tokenizer, the token counts are generally very similar across modern LLMs. Claude uses a BPE tokenizer with comparable results. For Gemini, counts may vary slightly more. For production applications where exact counts matter, we recommend using each provider's official tokenizer, but this tool provides excellent estimates for planning and cost estimation purposes.
What is a context window and why does it matter?
A context window is the maximum number of tokens a model can process in a single request, including both input and output. For example, GPT-4 Turbo's 128K context window means it can handle approximately 96,000 words. If your prompt plus expected response exceeds this limit, you'll get an error. Long documents may need to be chunked - our Text Chunker tool can help with this.
Understanding Tokenization for AI Development
As AI and large language models become central to modern software development, understanding tokenization is no longer optional—it's a core skill. Whether you're building chatbots, content generation systems, or AI-powered features, token awareness directly impacts your application's cost, performance, and reliability.
Modern LLMs like GPT-4, Claude, and Gemini use Byte Pair Encoding (BPE) or similar algorithms to convert text into tokens. This method was chosen because it balances vocabulary size with the ability to handle unknown words—crucial for multilingual support and technical content.
For AI engineers and developers, this token counter is an essential tool in your workflow. Use it to audit prompts before deployment, estimate project costs during planning, and debug tokenization issues when output seems truncated or incorrect. Combined with our other AI development tools, you have everything needed to build production-ready AI applications.