Recent evaluations show that the news is bad for the performance of AI coding assistants—specifically ChatGPT and GPT-4 based versions. The tests assayed how well each model fixed broken code. While they proved the older models were consistently reliable, the newer iterations are increasingly showing alarming inconsistencies. What does this mean for the reliability of AI tools going forward? As these new tools increasingly find themselves embedded into software development workflows, we need to examine their tangible impacts.
We ran all nine variants of ChatGPT through the same error message, informing it that one of the columns expected was missing from a given dataset. For our testing, we brought in the newest model, GPT-5. Observers immediately began to notice that where GPT-4 might have given a useful answer every time, newer models didn’t maintain that reliability. This lack of consistency in responses has raised alarms among developers who use these tools to save time and increase productivity on coding tasks.
Testing Methodology
The experiment’s design aimed to assess how well each model could address a specific coding issue related to a missing data column. While the error message itself was clear, the output ranged widely in quality and accuracy among the models evaluated. Each iteration of ChatGPT was tested on its understanding of the prompt and generation of correct code answers.
All together, that made for a ten run assessment for each model to classify their outputs as helpful, useless, or counterproductive. GPT-4 far outperformed the other AIs we tested every single time. It showed that it can do a great job addressing the challenge presented. Newer models exhibited a concerning trend: sometimes they solved the problem, but often they merely obscured the issue without providing a clear solution.
Performance Discrepancies
Over the course of the testing process, I found one thing overwhelmingly surprising. In three cases, ChatGPT just outright refused to follow explicit instructions to only output code. Instead it just printed out every column name in the dataframe. It advised implementation of the check for the missing required column. As logical as this approach may appear on the surface, it did not truly fix the root of the problem.
Additionally, even when the code they generated ran without errors, it sometimes returned non-deterministic output. This unpredictability serves to erode developers’ trust in these tools and exposes an inherent weakness in their usefulness. With the growing adoption of AI-generated code in software development, even minor inconsistencies would cause major setbacks and aggravation for end-users.
GPT-5 stood out by 100% of the time identifying effective solutions that actually got the job done. Yet it took an extremely practical approach by counting the rows using the actual index of each row and just adding 1 to it. This strategy worked successfully each time we tested it. It demonstrated the potential effectiveness of this new model, despite the overall downturn being experienced with other variants.
The Broader Implications
The trends mentioned in these evaluations are in line with even more macro worries from the AI advancement partnership. This occurred for a number of key models in 2025. Signs are pointing to a drop in their landmark success. This stagnation brings up very serious questions as to the fate of AI coding assistants and their place in the future of software development.
The diminishing value of these tools might make the productivity-sapping phenomenon worse for developers who have learned to depend on them. Prior to these evaluations, many reported that using LLM-generated code reduced task completion times from around 10 hours to approximately 7 or 8 hours. If the newer models don’t show sufficient staying power or better results developers may have to go back to the old ways. This might result in developers wasting a lot of time debugging errors.
As AI tools continue to evolve, ongoing assessments will be crucial to ensure they meet the needs of users effectively. The discrepancies noted in recent testing serve as a reminder that while advancements in technology can offer significant benefits, they require rigorous evaluation to maintain their value in practical applications.