In recent months, the performance of AI coding assistants has noticeably eroded, casting doubt on their dependability. Analysis on other models, including the recently released GPT-4 and yet more advanced GPT-5, revealed a more troubling trend. These models performed poorly at providing useful help to programmers. This decline is exacerbated by Claude’s impressive abilities, particularly older versions of Claude. These models are not well trained to successfully tackle tricky coding challenges. As developers continue to adopt these tools, it is important to understand both their potential and their limitations.
After developing our ten sample cases, we ran them through GPT-4 to see how consistently it produced the desired outcome. And the model, every single time, yielded a practical answer in return, reaffirming the model’s dependability. GPT-5 exhibited a mixed performance: while it successfully found solutions for all test cases, its methods varied. These discrepancies serve to illustrate a troubling rub with the efficacy of AI coding assistants.
Performance Comparison: GPT-4 and GPT-5
This isn’t the first time we’ve tested it. Our original testing found consistently helpful results each time we ran GPT-4. This model is mainly inspired by itself and its offspring, the much more recent GPT-5. Even with consistent success, considering how AI coding assistants have evolved may have led to more favorable outcomes. In real-world scenarios, GPT-4 assumed a guiding role at Carrington Labs. Its performance under the hood was crucial to providing a successful operation.
GPT-5 approached all of these issues with a new lens. Specifically, it creatively replaced the index of each row with that value plus 1 to create a replacement column. This simple but powerful approach proved effective in all test cases, showing its usefulness to be potentially vast. The model encountered some limitations throughout its execution. In 6 cases, it tried to execute code, including try/excepts that led to error messages or populated the new column with errors when the data was absent.
Curiously though, on the tenth try, GPT-5 just repeated the original code without offering additional interpretation. This is troubling both for the safety of the model and its ability to adapt and respond to a diversity of coding tasks. This disparity in performance between different deployments highlights that even as incredible progress in AI marches on, that doesn’t necessarily lead to better results for developers.
The Challenges with Older and Newer Models
An additional layer to the analysis was provided by the performance of some of the older AI models, in particular the Claude series. When presented with unresolvable tasks, these models would seem largely useless, often taking a “shoulder shrug” approach to coding conundrums. This lack of capacity could put a huge burden on developers who depend on these tools to help with the hardest parts of their work.
Even in new models such as GPT-5 the same approach would solve it more efficiently. At times, they missed the forest for the trees. This lack of uniformity can be maddening to developers who rely on these assistants to make their workflows more efficient. Tasks that would have taken five hours with AI assistance and ten hours without it are now typically taking seven or eight hours. This worrying trend can even be worse than that, resulting in longer time to completion. This change is a sign that developers are becoming more concerned about the decreased productivity promised by AI tools.
As these trends continue into 2025, many core models have reached a quality plateau, with some even declining in performance. This stagnation proves dangerous territory for developers in search of reliable coding support. This performance plateau is a sign AI coding assistants are due for an infusion of new innovation. We could do with a reconsideration of what they do for the software development industry.
Implications for Developers and Future Directions
For developers themselves, the implications are huge as they learn to work in the rapidly changing world of AI powered coding assistants. The tests across nine different versions of ChatGPT showed amazing drivesability or waterability. They revealed glaring errors produced by these models. The associated origin error message was created and pushed to every version. This underscores the importance of developers to adequately supervise AI outputs.
As use of AI coding assistants becomes more prevalent, it’s important for developers to proactively reconfigure their workflows to ensure productive use. For more complex tasks, they’ll need to arm themselves with project-specific expertise and market judgment to augment the AI aid to deliver successful project outcomes. Understanding these tools’ limitations will be key to staying productive and preventing the perils of AI-generated solutions.
