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Welcome to Tokenizador

Tokenizador is a professional AI tokenization analyzer that helps you visualize how different AI models process and tokenize your text. Whether you’re optimizing prompts, calculating costs, or comparing models, Tokenizador gives you instant insights into token usage.
Live Demo Available: Try Tokenizador instantly at tokenizador.alblandino.com - no installation or signup required.

Quick Start

1

Access the Application

Visit tokenizador.alblandino.com in your web browser. The application works on all modern browsers and devices.
Tokenizador is a fully client-side application - all tokenization happens in your browser, ensuring your text stays private.
2

Select an AI Model

Choose from 48 supported AI models using the dropdown selector. Models are organized by provider:

OpenAI

GPT-4o, GPT-4o Mini, GPT-4 Turbo, GPT-4, GPT-3.5 Turbo

Anthropic

Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku

Google

Gemini 1.5 Pro, Gemini 1.5 Flash

Meta

Llama 3.1 (405B/70B/8B), Llama 3 (70B/8B)
Plus models from Mistral AI, Cohere, Alibaba, DeepSeek, Microsoft, xAI, Amazon, NVIDIA, and many more!
3

Enter Your Text

Type or paste your text into the input area. Tokenization happens instantly as you type - no need to click any buttons.
Example Input
Texto a analizar...
Try different languages! The tokenizer works with any text including Spanish, English, Chinese, emojis, and code.
4

View Results

Watch as Tokenizador displays comprehensive analytics in real-time:
  • Token Count: Total tokens the model will process
  • Character Count: Total characters including spaces
  • Word Count: Number of words detected
  • Cost Estimate: Precise cost calculation based on the model’s pricing
The results section (results-section) automatically updates as you type, showing:
  • Live statistics in an interactive grid
  • Colorful token visualization
  • Detailed token list with individual tokens
  • Model-specific information

Understanding the Interface

Statistics Dashboard

The stats grid displays four key metrics in real-time:

Total Tokens

Exact token count using tiktoken library for maximum accuracy

Characters

Total character count including spaces and special characters

Words

Intelligent word count with proper text segmentation

Cost Estimate

Real-time cost calculation in USD based on official pricing

Token Visualization

Tokenizador provides two complementary views of your tokenized text:
Colored Token Display - Each token is shown with a unique color, making it easy to see how the model segments your text.The tokens-container displays tokens inline, helping you understand:
  • How words are split into sub-word tokens
  • How spaces and punctuation are handled
  • The efficiency of different text patterns

Model Information

For each selected model, Tokenizador displays:
  • Model Name: Full name and provider
  • Context Limit: Maximum tokens the model can process
  • Tokenization Type: The encoding algorithm used (e.g., cl100k_base, o200k_base)
  • Active Algorithm: Real-time display of the tokenization method
  • Cost Details: Input and output costs per 1M tokens
  • External Link: Direct link to Artificial Analysis for detailed benchmarks
The tokenization service automatically selects the appropriate encoding based on your chosen model. OpenAI’s newer models (GPT-4o, GPT-4o Mini) use o200k_base, while most others use cl100k_base.

Practical Examples

Example 1: Simple Text Analysis

Hola mundo

Example 2: Cost Comparison

When analyzing the same text across different models:
ModelTokensInput Cost (per 1M)Output Cost (per 1M)
GPT-4o~100$2.50$10.00
GPT-4o Mini~100$0.15$0.60
Claude 3.5 Sonnet~110$3.00$15.00
Llama 3.1 8B~95$0.055$0.055
Notice how different models may produce slightly different token counts for the same text due to different tokenization algorithms.

Example 3: Context Limit Awareness

Understanding Context Limits
Model: GPT-4Context Limit: 8,192 tokens
Model: GPT-4 TurboContext Limit: 128,000 tokens
Model: Claude 3.5 SonnetContext Limit: 200,000 tokens
Model: Gemini 1.5 ProContext Limit: 2,097,152 tokens
Tokenizador will alert you when your text approaches or exceeds a model’s context limit, helping you avoid API errors.

Advanced Features

Clear Function

Use the Clear button to quickly reset the input and start fresh:
Clear Implementation
// Clears text input and resets all displays
document.getElementById('clear-btn').addEventListener('click', () => {
    analyzer.handleClear();
});

Real-Time Analysis

The application uses a sophisticated debouncing system to analyze text as you type without overwhelming your browser:
Real-Time Analysis
async handleTextChange() {
    await this.performRealTimeAnalysis();
}

async performRealTimeAnalysis() {
    const text = this.uiController.getTextInput().trim();
    const selectedModel = this.uiController.getSelectedModel();
    
    if (!text) {
        this.resetDisplays();
        return;
    }
    
    const tokenResult = await this.tokenizationService.tokenizeText(text, selectedModel);
    const statistics = this.statisticsCalculator.calculateStatistics(
        text, tokenResult, selectedModel
    );
    
    this.updateDisplays(tokenResult, statistics);
}

Model Comparison

Switch between models instantly to compare how different tokenizers handle your text:
1

Enter your text once

Type your prompt or content in the text area
2

Switch models from the dropdown

Select different models to see how they tokenize the same text
3

Compare the results

Notice differences in token count, cost, and tokenization patterns

Tips for Effective Use

  • Shorter is better: More concise prompts use fewer tokens and cost less
  • Watch for patterns: Some phrases tokenize more efficiently than others
  • Test variations: Try different wordings to find the most token-efficient version
  • Different models have different token ratios (visible in MODELS_DATA)
  • GPT-4o with o200k_base encoding is often more efficient than cl100k_base models
  • Llama models tend to use slightly fewer tokens (0.95x ratio)
  • Mini/Small variants cost significantly less: GPT-4o Mini is ~17x cheaper than GPT-4o
  • Consider input vs output costs: Some models charge differently for generation
  • Open source models (Llama, Mistral) via APIs are often the most economical
  • Spaces are often separate tokens
  • Common words may be single tokens, while rare words split into multiple
  • Special characters and emojis may use multiple tokens
  • Code typically uses more tokens than natural language

Browser Compatibility

Tokenizador works on all modern browsers:
  • ✅ Chrome/Edge 90+
  • ✅ Firefox 88+
  • ✅ Safari 14+
  • ✅ Opera 76+
JavaScript must be enabled for the application to function. The app uses the tiktoken library loaded via CDN with automatic fallback mechanisms.

Architecture Overview

Tokenizador uses a modular architecture for maximum maintainability:
Modular Structure
// Main application class
class TokenAnalyzer {
    constructor() {
        this.tokenizationService = new TokenizationService();
        this.uiController = new UIController();
        this.statisticsCalculator = new StatisticsCalculator();
        this.init();
    }
}

// Initialized on page load
document.addEventListener('DOMContentLoaded', () => {
    const analyzer = new TokenAnalyzer();
});
The application consists of:
  • Configuration (models-config.js): All 48 models with pricing and specs
  • Services (tokenization-service.js): Core tokenization logic using tiktoken
  • Controllers (ui-controller.js): DOM manipulation and event handling
  • Utils (statistics-calculator.js): Token counting and cost calculations
  • Main App (token-analyzer.js): Orchestrates all components

Next Steps

Start Analyzing

Visit the live demo and start analyzing your AI prompts

Model Reference

Explore detailed information about all 48 supported models

Understanding Tokens

Learn more about how tokenization works across different models

Cost Calculator

Deep dive into cost estimation and optimization strategies

Troubleshooting

Make sure you’ve entered text in the input area. The application requires at least one character to begin tokenization. Check your browser console for any errors with the tiktoken library.
Different models use different tokenization algorithms. The counts shown are accurate for each specific model. Claude models use an approximation of cl100k_base which may differ slightly from their actual tokenizer.
For very large texts (>10,000 tokens), tokenization may take a moment. The application includes a loading indicator during processing. Consider analyzing smaller chunks for better performance.
Ensure the model data is loaded correctly. All model configurations are defined in models-config.js with details like context limits, costs, and URLs. Refresh the page if information doesn’t appear.

Questions or Feedback?

Tokenizador is built by Alex Blandino. For questions, issues, or feature requests, visit the project repository or contact the developer.

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