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General Questions

Tokenization is the process AI models use to break down text into smaller units called “tokens.” These tokens are the building blocks that language models process and understand.Think of tokens as puzzle pieces:
  • A token can be a whole word (e.g., “hello”)
  • A token can be part of a word (e.g., “un” + “believable”)
  • A token can be a punctuation mark or space
  • Numbers and special characters are also tokenized
Why it matters:
  • API costs are calculated per token
  • Models have maximum token limits (context windows)
  • Different models tokenize the same text differently
On average, one token equals approximately 4 characters of English text, or roughly 0.75 words.
Different AI models use different tokenization algorithms and vocabularies, which leads to varying token counts for the same text.Key factors:
  1. Tokenizer Algorithm: Each model uses its own encoding strategy
    • OpenAI GPT-4o uses o200k_base
    • OpenAI GPT-4/3.5 use cl100k_base (BPE)
    • Claude models have approximately 20% more tokens
    • Llama models have approximately 15% fewer tokens
  2. Vocabulary Size: Larger vocabularies can represent text with fewer tokens
  3. Language Optimization: Some tokenizers work better with certain languages
Example from models-config.js:
'claude-3.5-sonnet': {
  tokenRatio: 1.1  // 10% more tokens than GPT baseline
},
'llama-3.1-405b': {
  tokenRatio: 0.95  // 5% fewer tokens than GPT baseline
}
Tokenizador applies model-specific ratios to provide accurate token counts for each of the 48 supported models.
The cost estimates in Tokenizador are based on current pricing from AI providers and are highly accurate for input tokens.What’s included:
  • Real-time pricing per 1M tokens
  • Model-specific input and output costs
  • Accurate token counts using the tiktoken library
Important notes:
The displayed cost is for input tokens only. Output costs are typically higher and depend on the length of the model’s response.
Cost calculation from models-config.js:
'gpt-4o': {
  inputCost: 2.50,   // $2.50 per 1M input tokens
  outputCost: 10.00  // $10.00 per 1M output tokens
}
Prices are pulled from official provider pricing pages and artificialanalysis.ai.
Use the cost estimates to compare model efficiency. Sometimes a more expensive model with better tokenization can be more cost-effective!
Partially, yes - but with limitations.What works offline:
  • The core application interface
  • Model selection and configuration
  • Basic text input functionality
What requires internet:
  • Tiktoken library (loaded from CDN)
  • Font Awesome icons (loaded from CDN)
  • Google Fonts (loaded from Google’s CDN)
Fallback mechanism:Tokenizador includes a fallback tokenization system (see index.html:76-144) that activates if the tiktoken library fails to load:
// Fallback implementation if tiktoken doesn't load
window.tiktoken = {
  get_encoding: function(encoding) {
    return {
      encode: function(text) {
        // Basic estimation algorithm
        // Splits words and estimates tokens
      }
    }
  }
}
The fallback provides approximate token counts but won’t have the precision of the actual tiktoken library.
For true offline use: You would need to self-host the tiktoken library and other CDN resources.
Tokenizador is built with modern web standards and supports all current browsers.Fully supported:
  • ✅ Chrome/Edge (v90+) - Recommended
  • ✅ Firefox (v88+)
  • ✅ Safari (v14+)
  • ✅ Opera (v76+)
Mobile browsers:
  • ✅ Chrome Mobile
  • ✅ Safari iOS (v14+)
  • ✅ Samsung Internet
  • ✅ Firefox Mobile
Requirements:
  • JavaScript must be enabled
  • HTML5 support required
  • Modern CSS support (Grid, Flexbox)
Features used:
  • ES6+ JavaScript (classes, async/await, arrow functions)
  • Fetch API for resource loading
  • localStorage for potential future features
  • CSS custom properties (variables)
The app is fully responsive and works great on tablets and phones thanks to the mobile-first design approach.
Testing meta tag from index.html:
<meta name="apple-mobile-web-app-capable" content="yes">
Tokenizador stands out with several unique features:1. Extensive Model Support (48 models)
  • OpenAI, Anthropic, Google, Meta, Mistral AI
  • Plus 14 more providers including xAI, Amazon, NVIDIA, IBM
  • Most token counters only support OpenAI models
2. Real Token IDs
  • Uses the official tiktoken library
  • Shows actual token IDs, not approximations
  • Provides accurate token visualization
3. Interactive Visualization
  • Color-coded tokens by type
  • Hover to see individual token details
  • Visual token breakdown with IDs
4. Cost Estimation
  • Real-time cost calculation
  • Model-specific pricing
  • Updated from artificialanalysis.ai
5. Context Warnings
  • Alerts when approaching model limits
  • Shows context window for each model
  • Helps prevent truncated inputs
6. Professional Architecture
token-analyzer.js          → Main orchestration
tokenization-service.js    → Tiktoken integration
ui-controller.js           → Interface management
statistics-calculator.js   → Metrics & analysis
models-config.js          → 48 model definitions
7. Open Source & Free
  • No API keys required
  • No registration needed
  • Client-side processing (privacy-focused)
  • Available on GitHub

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Technical Questions

Tokenizador uses the tiktoken library with specific encodings for different model families.Primary Encodings:
EncodingModelsDescription
o200k_baseGPT-4o, GPT-4o MiniLatest OpenAI encoding
cl100k_baseGPT-4, GPT-3.5, Claude, Gemini, Llama, etc.Standard BPE encoding
From models-config.js:
MODEL_ENCODINGS = {
  'gpt-4o': 'o200k_base',
  'gpt-4o-mini': 'o200k_base',
  'gpt-4': 'cl100k_base',
  'claude-3.5-sonnet': 'cl100k_base', // Approximation
  'llama-3.1-405b': 'cl100k_base',    // Approximation
}
Non-OpenAI models use cl100k_base as an approximation with model-specific ratios applied to match actual tokenization behavior.
Yes! Tokenizador is built with a modular architecture that’s easy to integrate.Using the classes directly:
// Initialize the analyzer
const analyzer = new TokenAnalyzer();

// Wait for initialization
await analyzer.tokenizationService.waitForInitialization();

// Tokenize text
const result = await analyzer.tokenizationService.tokenizeText(
  "Your text here",
  "gpt-4o"
);

console.log(result.tokens); // Array of token objects
console.log(result.count);  // Total token count
Export functionality:
// Export results in different formats
const jsonData = await analyzer.exportResults('json');
const csvData = await analyzer.exportResults('csv');
const txtData = await analyzer.exportResults('txt');
Compare models:
const comparison = await analyzer.compareModels([
  'gpt-4o',
  'claude-3.5-sonnet',
  'llama-3.1-70b'
]);

View Source Code

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Tokenizador is privacy-focused and processes everything client-side.What we collect:
  • Anonymous usage analytics via Google Analytics
  • Page views and interaction events
  • No personal information
  • No text content you analyze
What we DON’T collect:
  • ❌ Your input text
  • ❌ Tokenization results
  • ❌ Personal information
  • ❌ IP addresses (beyond GA anonymization)
  • ❌ Authentication data (no accounts needed)
From index.html analytics setup:
// Track important user interactions
trackEvent('model_selected', 'Model Selection', modelName);
trackEvent('text_analyzed', 'Text Analysis', 'characters', length);
All tokenization happens in your browser. Your text never leaves your device.
Model pricing is configured manually and updated periodically.Current approach:
  • Pricing is hardcoded in models-config.js
  • Updated when providers change pricing
  • Cross-referenced with artificialanalysis.ai
Verification: Each model includes a direct link to Artificial Analysis for the most current pricing:
'gpt-4o': {
  inputCost: 2.50,
  outputCost: 10.00,
  url: 'https://artificialanalysis.ai/models/gpt-4o'
}
Click the “Ver en Artificial Analysis” link on any model to check the latest official pricing.

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