GPT-4o vs GPT-4: Tokenization Differences

Introduction

When OpenAI released GPT-4o in May 2024, it didn't just bring improved performance and multimodal capabilities—it also introduced a new tokenization system. The shift from cl100k_base (used in GPT-4) to o200k_base (used in GPT-4o) represents a significant evolution in how these models process text.

What Are o200k_base and cl100k_base?

Both are tokenization encodings developed by OpenAI, but they differ in their vocabulary size and optimization approach:

cl100k_base (GPT-4)

• Vocabulary size: ~100,000 tokens
• Used by: GPT-4, GPT-3.5-turbo, text-embedding-ada-002
• Optimized for: General English text and common programming languages
• Released: March 2023

o200k_base (GPT-4o)

• Vocabulary size: ~200,000 tokens
• Used by: GPT-4o, GPT-4o-mini
• Optimized for: Multilingual text, improved efficiency, multimodal content
• Released: May 2024

Key Differences in Practice

1. Vocabulary Size and Efficiency

The most obvious difference is the doubled vocabulary size. This expansion allows o200k_base to:

• Represent more concepts with single tokens: Common phrases, technical terms, and multilingual content often require fewer tokens
• Handle non-English languages better: Improved tokenization for languages like Chinese, Arabic, and others that were less efficient in cl100k_base
• Process code more efficiently: Better representation of common programming patterns and syntax

2. Multilingual Performance

One of the most significant improvements in o200k_base is its multilingual capabilities:

3. Token Efficiency Comparison

For most English text, o200k_base provides marginal improvements, but the gains are more pronounced for:

• Technical documentation
• Code comments and documentation
• Mixed-language content
• Specialized terminology

Performance Implications

Cost Considerations

Since OpenAI's pricing is based on token count, more efficient tokenization can lead to cost savings:

• Input costs: Fewer tokens for the same content means lower input costs
• Output costs: More efficient generation can reduce output token counts
• Context window utilization: Better token efficiency allows for more content within the same context limit

Processing Speed

Fewer tokens don't just mean lower costs—they also mean faster processing:

• Reduced computation time for both encoding and decoding
• Lower memory usage during processing
• Faster response times for applications

When to Use Each Tokenizer

Choose cl100k_base (GPT-4) When:

• Working primarily with English content
• Using established workflows that depend on consistent tokenization
• Budget constraints make GPT-4o cost-prohibitive
• Your application requires the specific capabilities of GPT-4

Choose o200k_base (GPT-4o) When:

• Working with multilingual content
• Processing large volumes of text where efficiency matters
• Building applications that benefit from multimodal capabilities
• Token optimization is crucial for your use case

Migration Considerations

If you're considering migrating from GPT-4 to GPT-4o, keep these factors in mind:

1. Token Count Changes

• Your existing prompts will likely use fewer tokens with o200k_base
• Update your cost calculations and monitoring systems
• Review context window utilization patterns

2. Application Integration

• Test thoroughly with your specific content types
• Update any hardcoded token limits or expectations
• Consider gradual migration rather than immediate switching

3. Performance Monitoring

• Track token usage patterns before and after migration
• Monitor response quality and consistency
• Measure actual cost and performance improvements

Practical Examples

Code Documentation

/**
 * Calculates the total cost of API requests
 * @param {number} inputTokens - Number of input tokens
 * @param {number} outputTokens - Number of output tokens
 * @returns {number} Total cost in USD
 */
function calculateApiCost(inputTokens, outputTokens) {
  const inputCost = inputTokens * 0.00003;
  const outputCost = outputTokens * 0.00006;
  return inputCost + outputCost;
}

Technical Content

Best Practices for Tokenization

1. Content Optimization

• Use consistent terminology throughout your prompts
• Avoid unnecessary repetition of common phrases
• Consider the tokenization efficiency of your content structure

2. Testing and Validation

• Use tokenization tools to analyze your content before deployment
• Test with representative samples of your actual data
• Monitor token usage patterns in production

3. Cost Management

• Factor tokenization efficiency into your model selection
• Consider the total cost of ownership, not just per-token pricing
• Implement monitoring to track token usage trends

Future Considerations

As AI models continue to evolve, we can expect further improvements in tokenization:

• Increased vocabulary sizes: Future models may use even larger vocabularies for better efficiency
• Domain-specific tokenizers: Specialized tokenizers for specific industries or use cases
• Dynamic tokenization: Adaptive tokenization based on content type and context

Conclusion

The transition from cl100k_base to o200k_base represents a significant step forward in tokenization technology. While the improvements for English text are modest, the gains for multilingual content, code, and technical documentation are substantial.

For most applications, GPT-4o's o200k_base tokenizer offers better efficiency and performance, making it the preferred choice for new projects. However, existing GPT-4 implementations can continue to work effectively with cl100k_base, especially for primarily English content.

The key is to evaluate your specific use case, test thoroughly with your content, and monitor the actual performance and cost implications of your tokenization choice.