Comparing Tokenization Across Different AI Models

One of the most important things to understand when working with multiple LLMs is that they don't tokenize text the same way. The same prompt can result in 100 tokens for one model and 150 for another. In this article, we'll explore the differences in how popular models tokenize text and what those differences mean for your applications.

The Tokenizer Landscape

Today's LLM ecosystem includes various tokenizers:

OpenAI models (GPT): Use the Tiktoken tokenizer with cl100k_base or o200k_base encoding
Meta Llama: Uses the SentencePiece tokenizer
Alibaba Qwen: Custom tokenizer optimized for multilingual content
Mistral: Uses v1 and v2 tokenizers depending on model version
Claude: Uses Anthropic's proprietary tokenizer

Practical Comparison: "Hello, how are you?"

Let's look at how different models tokenize a simple phrase:

Model	Token Count	Tokenization Approach
GPT-4	7 tokens	BPE (Byte Pair Encoding)
Llama 2	8 tokens	SentencePiece
Qwen	6 tokens	Custom
Mistral	7 tokens	Custom BPE variant

As you can see, even for a simple phrase, the token counts vary. This variation becomes more significant with longer texts.

Factors That Affect Tokenization Differences

1. Vocabulary Size

Different models have different vocabulary sizes. Larger vocabularies can represent more "complete" words as single tokens, resulting in fewer tokens overall.

2. Training Data

Models trained on different datasets will develop different tokenizers. A model trained heavily on code might tokenize code more efficiently than a general-purpose model.

3. Language Focus

Models optimized for languages like Chinese or Arabic might tokenize these languages more efficiently than Western language-focused models.

What This Means for You

For Cost Optimization:

If you're cost-conscious, Qwen in this example tokenizes more efficiently. However, you should also consider model quality and latency.

For Multi-Model Applications:

If your application switches between models, always recalculate token counts. Your context window constraints might change!

For Prompt Engineering:

The optimal prompt structure might vary across models due to different tokenization patterns. Test your prompts with each model you plan to use.

Special Characters and Multilingual Content

Tokenization differences are even more pronounced with:

Emojis: Some models handle emojis efficiently, others use multiple tokens
Code: Different models have different levels of optimization for code
Asian languages: Tokenization varies significantly across models
URLs and emails: Different tokenizers handle these differently

Best Practices for Model Comparison

✓ Do:

Test your actual use case with each model
Compare not just tokens, but also quality and latency
Consider the total cost: tokens × price per token
Use tools like Tiktokenizer to visualize differences

✗ Don't:

Assume token counts will be similar across models
Optimize for one model without testing others
Ignore special characters in your token estimates
Forget to re-test when updating model versions

Conclusion

Understanding the differences in how various models tokenize text is crucial for building efficient, cost-effective AI applications. While these differences might seem small for simple prompts, they compound quickly with longer contexts and higher volumes.

Take advantage of tools that let you visualize and compare tokenization across models, test thoroughly with your specific use cases, and always measure rather than assume.