Why I Prefer ChatGPT 4 Over ChatGPT 4o: A Case Against Sacrificing Quality for Speed

Since the launch of ChatGPT 4 on March 14, 2023, users and developers have been exploring the strengths and weaknesses of this advanced model. Now, with the release of ChatGPT 4o, there's a new twist in the story. This newer model is faster, but does it sacrifice too much quality for speed? Today's discussion focuses on a key question: Have we reached a limit in AI innovation until ChatGPT 5 arrives, or is there exciting news just around the corner?

ChatGPT 4o prioritizes speed but seems to lose some of the depth and detail that ChatGPT 4 is known for. This raises important questions about the strategy behind AI development. Why would OpenAI release a faster but less detailed version? Is this trade-off between speed and quality really necessary in today's AI world, or does it reflect a bigger challenge in developing AI?

As technology advances rapidly, finding the right balance between improving performance features like speed and maintaining or enhancing the quality of responses is crucial. This article looks at whether focusing on speed meets the actual needs and demands of users and examines the impact of such decisions on the future of AI innovations. Are we just waiting for the next big development in AI, or have we reached a temporary stopping point in the progress of generative models?

This article does not intend to overlook the significant new features of ChatGPT 4o .. but it all about "Inference"!

This article does not intend to overlook the significant new features of ChatGPT 4o, which indeed adds considerable value with capabilities like real-time audio interactions and image generation. These enhancements undoubtedly improve customer service and contribute to the creation of more dynamic marketing materials. However, I believe that "inference" — the core intelligence of any model — is so critical that it can make or break the model's effectiveness. Despite the impressive advancements in speed and multimodality, if the inference quality is compromised, it could significantly undermine the utility of the model in practical applications.

Understanding Inference: The Heart of AI Models

In the world of artificial intelligence, "inference" refers to the model's ability to apply knowledge and reasoning to new situations. It is the core of any AI's intelligence, allowing it to understand and respond to user queries accurately. If a model fails in inference, it loses much of its value, regardless of any other capabilities it might have. This is because inference is essentially the measurement of the model’s intelligence—the very feature that makes AI impressive and useful in real-world applications.

Why Inference Should Not Be Sacrificed for Speed

The emphasis on speed with the introduction of ChatGPT 4o has raised concerns. Faster response times are undoubtedly beneficial, particularly for simple tasks or when user demand is high. However, when this speed comes at the cost of inference quality, the trade-offs can be significant. A model that responds quickly but inaccurately or incoherently is less reliable and can even damage the reputation of AI technologies.

From my experience, I've noticed that ChatGPT 4o often does not understand problems as effectively as ChatGPT 4. This can be frustrating and may lead to mistrust in the model. Instances where ChatGPT 4o provides incorrect responses or exhibits higher levels of "hallucination" (producing made-up or irrelevant information) are particularly concerning. Such issues underline why strong inference capabilities are crucial—they ensure that an AI system remains a helpful and trusted tool rather than becoming a potential liability.

Advanced Users Lead, Mainstream Follows: Shifting Back to ChatGPT 4

Advanced AI users and engineers were quick to notice the differences between ChatGPT 4 and ChatGPT 4o, particularly when it came to the depth and reliability of inference. These experts have been vocal about their preference for the original model due to its robust handling of complex queries and critical tasks. Now, this awareness is spreading to general users, who are increasingly experiencing the limitations of ChatGPT 4o firsthand and starting to switch back to ChatGPT 4.

ChatGPT 4o is its tendency to prioritize response delivery over response quality

One of the notable criticisms of ChatGPT 4o is its tendency to prioritize response delivery over response quality. While it is less "lazy"—often more willing to provide direct answers—it sometimes glosses over the depth and thoroughness that ChatGPT 4 offers. For instance, while ChatGPT 4 might explain the process behind writing a program, offering educational value and insights into the steps involved, ChatGPT 4o might attempt to generate code directly. However, this directness can be a double-edged sword; the model might skip vital details or encourage users to complete complex parts themselves without adequate guidance.

This distinction becomes crucial for programmers and professionals engaged in critical tasks. In environments where precision and reliability are paramount—such as in coding critical software components or handling sensitive data—falling back on a tool that trades accuracy for speed is not viable. As a result, many users, especially those in technical fields, are reverting to using ChatGPT 4, which they trust to handle complex tasks with the necessary diligence and depth.

This shift underscores a broader trend: as AI tools become more integrated into professional and everyday scenarios, user expectations are maturing. People are not just looking for quick answers but solutions that are both accurate and informative, shaping a preference for quality over speed in AI interactions.

Speculations on the Slow Pace of New Releases

Since the launch of ChatGPT 4 on March 14, 2023, there hasn't been a major breakthrough in model upgrades, leading to speculation about the reasons behind this stagnation. It's possible that OpenAI could be developing a more advanced model but facing hurdles such as performance optimization, security concerns, or regulatory challenges, especially considering the geopolitical climate with countries like China and Russia.

There's also a possibility that the slow release cycle is intentional, allowing more time for thorough testing and refinement to avoid the pitfalls seen with rapid model updates. These factors combined could explain why a potentially superior model hasn't been released to the public yet.

Conclusion and Recommendation

Until we see significant improvements in inference capabilities, I advise users to approach ChatGPT 4o with caution. Relying on a model that often misunderstands queries or provides incorrect answers can be risky, especially in professional or critical settings. In some cases, alternative open-source models might offer more reliable outputs at this stage.

In the rapidly evolving field of AI, balancing innovation with reliability remains a key challenge. As users, staying informed and critically assessing the tools available to us can help ensure that we leverage AI technologies effectively and safely.