Understanding Token Usage Across Different LLMs

One of the most important aspects of using large language models (LLMs) is understanding how they count and charge for tokens. Each provider implements tokenization differently, which can significantly impact your costs and performance. In this article, we'll explore how various LLMs handle tokenization and provide tips to optimize your token usage.

What Are Tokens?

Tokens are the basic units that LLMs process. They can be parts of words, whole words, or even punctuation marks. For example, the sentence "Hello, how are you?" might be broken down into tokens like ["Hello", ",", "how", "are", "you", "?"].

Different models tokenize text differently:

• OpenAI's GPT models use a tokenizer called "tiktoken"
• Anthropic's Claude has its own proprietary tokenizer
• Google's Gemini uses SentencePiece with a different vocabulary
• Open-source models often use different versions of BPE or WordPiece tokenizers

Comparing Token Counts Across Models

We ran experiments with identical prompts across multiple models to compare token counts. Here's what we found:

Model	Tokens for "Hello world"	Tokens for a 500-word document
GPT-4	2	~750
Claude 3	3	~800
Gemini Pro	3	~780

As you can see, there are notable differences in how models tokenize the same text, which directly affects costs.

Cost Implications

These differences in tokenization have direct cost implications. For example, if Claude consistently uses 10% more tokens than GPT-4 for your specific use case, that translates to a 10% higher cost for the same input.

However, cost optimization isn't just about using the model with the lowest token count. You also need to consider:

• Response quality: A model that uses more tokens but provides better responses might be more cost-effective overall
• Token pricing: Different providers charge different rates per token
• Context length: Some tasks require longer context windows, which some models handle better than others

Optimizing Your Token Usage

Here are some strategies to optimize your token usage across all LLMs:

1. Be concise in your prompts: Remove unnecessary information or examples
2. Use compression techniques: For long documents, consider summarizing or extracting key points
3. Choose the right model for the task: Some models are more efficient for certain types of content
4. Monitor and analyze: Use TryAII to compare token usage across models for your specific prompts

Conclusion

Understanding token usage is crucial for optimizing your LLM costs. By using TryAII's comparison features, you can easily see how different models tokenize your specific content and make more informed decisions about which models to use for different tasks.

In our next article, we'll dive deeper into strategies for prompt optimization that can help reduce token usage while maintaining or improving output quality.