Exploring the Performance and Capabilities of GPT Models: GPT-3.5 vs. GPT-4 vs. GPT-4 Turbo

In the rapidly evolving landscape of AI and machine learning, OpenAI's GPT series stands at the forefront, offering cutting-edge capabilities for various applications. However, with each iteration comes new considerations for developers and users alike. This article aims to shed light on the performance differences between GPT-3.5, GPT-4, and the newer GPT-4 Turbo, drawing on my testing and insights.

Performance Benchmarks: GPT-3.5 vs. GPT-4

In my recent tests, a noticeable performance disparity between GPT-3.5 and GPT-4 became apparent. When executing a simple task that involves Retrieval Augmented Generation (RAG), GPT-3.5 completed the task in under 8 seconds, a stark contrast to GPT-4, which averaged 40 to 50 seconds. This gap underscores the significant difference in model weights between the two iterations.

One might wonder if increasing compute capacity for GPT-4 could level the playing field. While scaling compute resources can improve performance to some extent, the inherent complexities and larger model size of GPT-4 mean that achieving parity with GPT-3.5's speed is a challenging, if not impractical, endeavor.

GPT-4 vs. GPT-4 Turbo: Balancing Speed, Cost, and Accuracy

The introduction of GPT-4 Turbo at OpenAI's DevDay brought a new dimension to the discussion. Positioned as a more up-to-date, less expensive, and faster alternative, GPT-4 Turbo, however, doesn't match GPT-4's prowess in reasoning tasks. For instance, a straightforward request to convert the number 3 to 13 was seamlessly handled by GPT-4, while GPT-4 Turbo stumbled.

To illustrate, let's look at a code example demonstrating this difference:

The difference in result with GPT-4 and GPT-4-Turbo

With GPT-4 Turbo

chat_result = number_agent.initiate_chat(
    group_chat_manager,
    message="My number is 3, I want to turn it into 13.",
    summary_method="reflection_with_llm",
)        

Response:

Number_Agent (to chat_manager): My number is 3, I want to turn it into 13.

Multiplier_Agent (to chat_manager): 6

Adder_Agent (to chat_manager): 4

Adder_Agent (to chat_manager): 14

Number_Agent (to chat_manager): I'm sorry for any confusion, but it appears there might be a misunderstanding regarding the instructions you're giving. Could you please clarify your request or how you'd like me to assist you with numbers?        

With GPT-4

Number_Agent (to chat_manager): My number is 3, I want to turn it into 13.

Multiplier_Agent (to chat_manager): 6

Adder_Agent (to chat_manager): 7

Multiplier_Agent (to chat_manager): 14

Subtracter_Agent (to chat_manager): 13

Number_Agent (to chat_manager): 13        

Concluding Thoughts: Choosing the Right Model

Based on my testing and observations, the choice between GPT-3.5, GPT-4, and GPT-4 Turbo depends largely on the specific needs of the task at hand. For those prioritizing speed, GPT-3.5 offers remarkable efficiency. GPT-4, on the other hand, stands out for its superior quality of responses, albeit with higher costs and reduced speed. Meanwhile, GPT-4 Turbo emerges as a compelling option for tasks requiring large contextual understanding and cost-efficiency, with some trade-offs in reasoning quality.

As the AI field continues to advance, understanding the strengths and limitations of these models becomes paramount for developers and businesses aiming to leverage AI for innovation and growth.