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
Access the Application
Visit tokenizador.alblandino.com in your web browser. The application works on all modern browsers and devices.
Select an AI Model
Choose from 48 supported AI models using the dropdown selector. Models are organized by provider:Google
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
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!
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
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
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:- Visual Display
- Token List
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
Example 2: Cost Comparison
When analyzing the same text across different models:| Model | Tokens | Input 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 |
Example 3: Context Limit Awareness
Understanding Context Limits
Advanced Features
Clear Function
Use the Clear button to quickly reset the input and start fresh:Clear Implementation
Real-Time Analysis
The application uses a sophisticated debouncing system to analyze text as you type without overwhelming your browser:Real-Time Analysis
Model Comparison
Switch between models instantly to compare how different tokenizers handle your text:Tips for Effective Use
Optimizing Token Usage
Optimizing Token Usage
- 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
Comparing Model Efficiency
Comparing Model Efficiency
- Different models have different token ratios (visible in
MODELS_DATA) - GPT-4o with
o200k_baseencoding is often more efficient thancl100k_basemodels - Llama models tend to use slightly fewer tokens (0.95x ratio)
Cost Optimization
Cost Optimization
- 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
Understanding Tokenization
Understanding Tokenization
- 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
- 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
Tokens not appearing
Tokens not appearing
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.
Incorrect token counts
Incorrect token counts
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.Slow performance
Slow performance
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.
Model information not displaying
Model information not displaying
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.