Digital Product Studio

Don’t Fall for OpenAI Marketing Hype: Early Tests Reveal That OpenAI Overstated The Coding Abilities of GPT-4o

When OpenAI dropped its new AI model, GPT-4o, it touted its “advanced coding skills.” However, some initial user experiences have pointed out flaws that don’t align with the marketing. OpenAI advertisements did not accurately characterize the GPT-4o programming skills based on real-world programming tasks. Reddit users reported that the company inflated GPT-4o’s coding abilities beyond its actual capabilities.

Basic Coding Mistakes Made by GPT-4o

Through discussions, users reported various mistakes made by GPT-4o when handling code. Below is a list of the mistakes made by GPT-4o in its coding capabilities:

1. Spelling Mistakes

Users have reported that GPT-4o introduced spelling mistakes in the generated code. These mistakes are more commonly seen in heavily quantized versions of the model.

2. Missing Parentheses

There were instances where GPT-4o omitted essential parentheses in the generated code, leading to syntax errors.

3. Typographical Errors

GPT-4o is misspelling variable names, introducing unnecessary characters or capitalization errors.

4. Incorrect Directory Paths

In some cases, GPT-4o hard-coded incorrect directory paths when modifying code, leading to issues in file access and execution.

5. Logic Errors

Moreover, users have encountered situations where GPT-4o got stuck in loops of bad logic, affecting the coherence and functionality of the generated code.

6. Inconsistent Output

GPT-4o has shown inconsistent performance, sometimes failing to retain critical parts of the established code when refactoring or critiquing it.

7. Basic Mistakes in CSS

Surprisingly, GPT-4o has struggled with basic CSS-related coding tasks, which its predecessor, GPT-4, handled without any problem.

8. Repetitive and Literal Interpretations

Additionally, the model has been noted to be repetitive in its responses and often fixated on literal meanings rather than implied meanings in the context of conversations.

9. Poor Content Creation

While GPT-4o is fast in generating code, the quality of the output for tasks related to content creation has been considered low, making it less useful despite its speed advantage.

10. Long Tail Training Data Handling

Lastly, GPT-4o seems to handle long-tail libraries better, but it has not shown significant improvement in coding tasks that do not involve these libraries.

These issues highlight the areas where GPT-4o’s performance in coding tasks falls short, ranging from basic syntax errors to deeper issues in logic and consistency.

More User Experiences with GPT-4o

Despite coding, the responses to more GPT-4o capabilities have been varied, with users sharing a mix of positive experiences and notable frustrations.

1. Complex Tasks and Instruction Following

Some users have reported that GPT-4o struggles with complex tasks and does not follow instructions. GPT-4o failed at creating unit tests and implementing mocks, tasks that GPT-4 handled well. This suggests that GPT-4o might be less reliable for detailed, multi-step technical tasks.

2. Coding and Technical Work

Opinions on GPT-4o’s coding capabilities vary. While some users find it worse than GPT-4 for specific tasks, others have had positive experiences. One user noted that GPT-4o provided better lateral thinking for general queries, while another mentioned that GPT-4o handled certain coding problems more effectively, particularly with logic and problem-solving in university assignments.

3. Performance and Speed

Many users appreciate the increased speed and reduced cost of GPT-4o. These improvements make it more accessible, especially for those who were previously using GPT-3.5. This indicates that GPT-4o can be a significant upgrade for general-purpose use, despite its limitations in specific coding tasks.

4. Language and Tokenization

Users have highlighted improvements in tokenization for various languages, allowing for more efficient text processing. This can be particularly beneficial for multilingual applications where token economy is crucial. However, there are still performance inconsistencies across different language contexts, indicating room for improvement.

5. Model Architecture

There is speculation among users about the underlying architecture of GPT-4o. Some believe that it might involve a mixture of smaller models, potentially including domain-specific GPT-2 variants, to achieve faster and cheaper performance. 

6. Benchmarking and Use Cases

While benchmarks and evaluations provide valuable insights, it’s important to consider the real-world testing and specific use cases when assessing the performance of GPT-4o. Some benchmarks may rate GPT-4o lower, but personal experiences vary significantly depending on the task at hand. For example, one user found GPT-4o exceptionally useful for guiding them through API calls in Unreal Engine, while others found it less reliable for long conversations and maintaining context.

GPT-4 vs GPT-4o

Independent researcher Bindu Reddy recently conducted an early evaluation of GPT-4o’s programming skills and shared enlightening results on X:

While GPT-4o is indeed faster as OpenAI claimed, Reddy found its success rate on complex tasks lagged significantly behind GPT-4:

  • GPT-4o: 79/96 total tasks, 52/65 coding tasks completed successfully
  • GPT-4: 90/96 total tasks, 60/65 coding tasks completed successfully

This implies that GPT-4 possesses stronger problem-solving and technical reasoning abilities, contradicting OpenAI’s narrative that GPT-4o surpasses previous models.

Final Verdict: The Need for More Evidence

While OpenAI’s marketing claims about GPT-4o may have sparked excitement, it is crucial to approach them with caution. Real user experiences have already revealed discrepancies between the claims and the model’s actual performance. As GPT-4o continues to develop, users will continue to share their experiences, helping to separate facts from hype. But for now, user experiences with GPT-4o shows that OpenAI overstated the model’s coding abilities.

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Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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