The recent tests have shown a disturbing trend with AI coding assistants. The latter including different iterations of GPT-4 and GPT-5, and even these have demonstrated uneven performance. These models were originally intended to assist developers in writing code. Unfortunately, new findings have revealed a dramatic decline in their quality and utility. This article goes into greater detail about the recent evaluation’s findings. It emphasizes their effectiveness in rectifying coding mistakes, particularly errors resulting from omitted factors within data structures.
In a direct comparison of controlled trials, the models faced off against a coding error. That’s because this error was tied to a lost column name in a dataframe. We specifically wanted to test their ability to process the problem solving and follow direction. Most importantly, both GPT-4 and GPT-5 were rigorously tested over 10+ trials each, uncovering invaluable details about their operation and limitations.
Performance Evaluation of GPT Models
After completing the initial evaluation process, we found that GPT-4 was able to consistently generate a useful answer in all ten attempts. This performance was a promising predictor of reliability to come, particularly in contrast to its successors. GPT-5 really showed its power as it always found a solution that worked great every single time. Its approach was to take the original index of each row and just increment that by 1. This straightforward approach highlights a significant aspect of AI coding assistants: while they may occasionally provide effective solutions, there is an underlying issue regarding their overall reliability.
The trials uncovered several shortcomings. In six other cases, both models tried to run the code but failed, returning exceptions where code needed to successfully return an output. Given such an inconsistency, one might wonder how robust they are at handling real-world coding problems. In three instances, the models returned only code despite explicit instruction to yield code only. This means they have a long way to go before they can actually interpret user intentions correctly.
Tests also uncovered that both models recommended looking for your required column to exist within your dataframe. On the surface, this sounds like a reasonable step to check during routine troubleshooting. It underlines a shortcoming in the model’s efforts to address the root cause directly rather than avoiding it.
A Decline in Quality Over Recent Months
Instead, over the past few months, all evidence points to a significantly deteriorated performance of AI models such as GPT-4 and GPT-5. Anecdotal evidence suggests that the majority of core models hit a quality plateau during 2025. For developers who have come to rely on these tools for smarter, more efficient coding solutions, this stagnation can be damaging.
The author, who happens to be CEO of Carrington Labs and heavily uses LLM-generated code, has seen this drop off up close and personal. As the software development industry continues to adopt AI coding assistants, developers are losing more than ever. The declining effectiveness of these tools is starting to undermine their efficiency and accuracy.
Previous models such as Claude had difficulty handling multi-step reasoning tasks. They always came across as brushing the challenge aside rather than offering up a solution. By contrast, the new models perform poorly. When they do propose something, they often miss the mark on key issues or avoid directly addressing shortcomings and just kick the can down the road without providing alternatives.
Implications for Developers and Future Directions
The ramifications of this research are significant for developers and companies that use AI to help write code. Inconsistent performance of models, such as GPT-4 and GPT-5. Regrettably, this inconsistency begs a better look at what their place should be in the software development lifecycle. Developers should be more mindful when using these tools to automate sensitive programming functions.
We know that AI technology is advancing quickly. Developers and researchers need to know the limitations of the models being released. AI usage shouldn’t come at the expense of basic programming knowledge or critical thinking—the two should be used in conjunction with one another. Advocacy organizations need to be on the cutting edge in understanding how these tools work and their effectiveness, and change their strategies to compensate.
