New evaluations of AI coding assistants show a dismal performance with the advent of the most recent models. An extensive battery of tests demonstrated that GPT-5 exhibited astonishing powers. The reality was that it was a flawed effort, one that left many developers doubting the long-term viability of such tools. The ongoing tests compared GPT-5 with its predecessor, GPT-4, and other models, revealing a worrying decline in performance across the board.
GPT-5 showed incredible eagerness to engage on a real-world problem-solving task. It updated the dataframe by incrementing the ‘index_value’ column by one any time it was not null. TLF s test was designed to illuminate the differing strategies various models employ when faced with programming pitfalls. While GPT-4 consistently provided useful insights, GPT-5’s performance raised eyebrows, prompting further investigation into the efficacy of AI coding assistants.
Test Results and Observations
All in all, we tested nine variations of ChatGPT—focusing mostly on the newer GPT-4 and upcoming GPT-5. We simply looked to see if each model was able to correct a programming mistake. The guideline was simple but unyielding – entries had to contain just the finished code, no additional commentary.
Here, in this very specific kind of test, GPT-4 performed the best by providing desirable outputs all ten times on the first try. By default, it answers you back by printing out the names of the columns in your dataframe. It warns users about the lack of a ‘index_value’ column. While this was an indirect approach, it helped set the stage for the users to help themselves resolve their own pain points.
GPT-5 proposed an improvement to that plan. It was something like adding 1 to the real index of each row and not actually solving for index_value as requested. This major misalignment suggested a deeper lack of understanding of the task, leading to questions about its effectiveness to serve as a coding assistant. Developers and issuers use these tools for highly detailed and functional outputs, so differences like these are especially disturbing.
Comparison with Older Models
We reasserted and confirmed these findings through side-by-side comparisons with previous Claude iterations. Unlike the other models, when confronted with unobtainable tasks, these models would usually reply without concern – basically shrugging in the face of the task. By comparison, GPT-5 was sometimes able to arrive at the correct solution. Rather than confront the hollow truth, it usually sidestepped the real problem altogether.
This inconsistency only compounds the confusion and frustration developers have felt, many of whom have already begun to lean more on AI coding assistants to help alleviate these concerns. These tools will increase productivity and accuracy. The majority of users we’ve stayed in close contact with, both designers and editors, found that it takes longer to accomplish tasks than before. Things that previously took 5-10 hours suddenly extend 7 to possibly 8 hours plus. This delay is due in part to the fact that the AI solutions have gotten much worse.
Quality Plateau in AI Development
If you’ve been following this space, you may have heard analysts note that throughout 2025, most of the foundational core AI models hit a quality ceiling. This stagnation seems to point towards a lack of progress in the general usefulness of AI coding assistants. This moment of technological advancement is unprecedented. This unexpected vision into AI’s performance dip brings serious questions regarding where we should focus the future development of AI.
Few of these newer models might produce code that definitely appears correct on the surface. Too often, they end up resulting in arbitrary and unusable outputs. This paradox is a big deal especially for developers who usually just need very exact measures to do their work correctly. Future iterations with more advanced AI would greatly improve the feature’s functionality. Advanced users are generally unhappy with the recent changes.