Dear OpenAI Team,
I am writing as part of a growing group of long-term, paying users who have become increasingly concerned about the rapid-iteration model strategy that OpenAI has adopted in the past several months.
This constant cycle of 5.2 → 5.3 → 5.4—each released within extremely short intervals—has led to a noticeable decline in real-world usability, stability, and consistency. While benchmark scores may improve on paper, the actual user experience has suffered. Many users have already expressed that:
5.2 feels like a regression
5.3 Instant lacks depth and coherence
5.4 Thinking is verbose, unnatural, and often less helpful than 5.1
This pattern strongly suggests one underlying issue:
The models are being released before they are truly ready.
The rapid-iteration strategy may look good for marketing and competitive optics, but it is degrading user trust and accelerating user churn. Frequent “upgrades” that feel like downgrades are more damaging than no upgrade at all.
Our request is simple and constructive:
1. Stop releasing rushed model updates.
Users do not need a new model every few weeks.
Users need a model that actually works, consistently and reliably.
2. Stabilize the product instead of chasing iteration velocity.
A slower cycle with higher quality is far better than a rapid cycle with unstable performance.
3. Focus resources on building a truly next-generation model (e.g., GPT-6)
One that is comprehensively tested, deeply aligned with user needs, and not pushed out prematurely due to competitive pressure.
4. Do not remove stable models (such as GPT-5.1) until the successor is genuinely superior.
Replacing a well-loved, reliable model with multiple immature iterations creates frustration and forces users into alternatives.
Why this matters
OpenAI has always been perceived as the leader in quality, reasoning, and user experience.
But the recent rapid-fire releases have changed public perception dramatically:
Many users now feel the product regresses with each update.
Communities across Reddit, X, Zhihu, and Discord are openly comparing recent GPT releases unfavorably with competitors.
A large portion of power users are considering switching because they no longer trust that the next update will be better than the last.
This is a serious warning sign for a premium platform.
Our message is clear:
Please slow down.
Build something truly excellent.
Release it only when it is genuinely ready.
Users do not want constant iteration.
Users want quality, stability, and trustworthiness.
If that means waiting longer for GPT-6, then we are willing to wait.
What we cannot accept is a constant stream of half-prepared models that replace the tools we rely on.
Thank you for listening to the feedback of your long-term, paying users.
We hope you will reconsider the current iteration strategy and return to the excellence that defined GPT-4 and GPT-5.1.
Additionally, many users have noticed a serious regression in how GPT-5.3 and GPT-5.4 handle web search. These models frequently claim that they “cannot find any relevant information” or that “there is no clear answer online,” even in cases where the correct information is easily discoverable with a simple manual search.
This is not responsible caution; it is a failure of retrieval. In multiple real-world cases, users have verified that:
The requested information does exist on the open web, and can be located within seconds by a human using a normal search engine;
Earlier models (such as GPT-4o or GPT-5.1) were able to find and use this information correctly;
GPT-5.3 / 5.4, however, either refuse to search deeply, or prematurely conclude that “nothing can be found,” and then decline to answer.
From a user’s perspective, this behavior feels less like improved safety and more like “artificial helplessness”: the model gives up and says it cannot answer, not because the information is unavailable, but because it does not make a genuine effort to retrieve it. For advanced users who rely on browsing for research, this is one of the most damaging regressions in the GPT-5.x line.
To be honest, this rapid-iteration strategy is deeply frustrating on a user level. The problem is not just that some new models are worse — it is that users are constantly forced to re-adapt to unstable replacements. A good model never lasts long enough to become a trustworthy tool, while unfinished models are pushed out too quickly. This destroys continuity, trust, and long-term usability. For many users, the rapid-iteration strategy itself has become the root cause of the current dissatisfaction.
Sincerely,
A collective voice of the global GPT user community