GPT-4 vs GPT-3.5: Tokenization and Cost Comparison

One of the most important decisions when using OpenAI's APIs is choosing between GPT-4 and GPT-3.5-turbo. While GPT-4 offers superior reasoning capabilities, GPT-3.5-turbo is faster and cheaper. But how do they differ in terms of tokenization? Let's dive deep into this comparison.

Tokenization Differences

Both GPT-4 and GPT-3.5-turbo use the same tokenizer (cl100k_base encoding), so the tokenization pattern is identical. However, the cost per token differs significantly.

Cost Comparison

Model	Input Cost	Output Cost
GPT-4	$0.03 / 1K tokens	$0.06 / 1K tokens
GPT-3.5-turbo	$0.0015 / 1K tokens	$0.002 / 1K tokens

As you can see, GPT-3.5-turbo is significantly cheaper - about 20 times cheaper for input tokens! This makes it the go-to choice for cost-sensitive applications.

When to Use Each Model

Use GPT-4 When:

You need complex reasoning and analysis
Handling specialized or technical content
Quality is more important than cost
Processing nuanced, context-dependent tasks

Use GPT-3.5-turbo When:

Building high-volume applications
Simple text generation or classification
Cost is a primary concern
Response time is critical

Real-World Cost Example

Imagine you're building a chatbot that processes 1 million user queries per month. Each query is about 500 tokens on average.

GPT-4:

Input: 1M × 500 tokens × $0.03 / 1K = $15,000
Output: 1M × 200 tokens × $0.06 / 1K = $12,000
Total: $27,000/month

GPT-3.5-turbo:

Input: 1M × 500 tokens × $0.0015 / 1K = $750
Output: 1M × 200 tokens × $0.002 / 1K = $400
Total: $1,150/month

That's a difference of $25,850 per month! Of course, you need to balance this with quality requirements.

Hybrid Approach Strategy

Many production applications use a hybrid approach:

Route simple queries to GPT-3.5-turbo - Fast and cheap
Use GPT-4 for complex requests - Better quality when needed
Implement fallback logic - Use GPT-3.5 first, retry with GPT-4 if needed
Cache frequent queries - Avoid API calls altogether

Conclusion

While GPT-4 and GPT-3.5-turbo use the same tokenizer, their cost-effectiveness varies dramatically. For cost-sensitive applications, GPT-3.5-turbo is the clear winner. For complex reasoning tasks, GPT-4's superior capabilities justify the higher cost. The best approach is understanding your use case and implementing a strategy that balances both.