Users Report Sudden Deterioration in GPT-5.2 Performance Amid Rollout Concerns
Multiple users across AI forums have reported a sharp decline in GPT-5.2’s reliability for basic tasks such as OCR processing and file generation, sparking widespread concern as OpenAI prepares for a major backend update. Experts warn that inconsistent behavior may signal underlying model instability.
Users Report Sudden Deterioration in GPT-5.2 Performance Amid Rollout Concerns
Over the past week, a growing number of users have taken to online forums to report a troubling decline in the performance of GPT-5.2, OpenAI’s latest large language model iteration. Users describe erratic behavior in tasks previously handled with consistency—such as generating plain text files from OCR inputs, correcting output errors, and producing downloadable .txt or .md files—only to encounter missing content, nonsensical responses, or abrupt model refusal. One Reddit user, posting under the username /u/DareToCMe, summed up the frustration: “It’s simply impossible to work with anything in GPT that needs any simple task… I’m going crazy because GPT for one week already.”
While OpenAI has not issued an official statement regarding the anomalies, internal sources familiar with the rollout confirm that a major backend update is underway, involving significant retraining of the model’s tokenization and output-generation pipelines. The update, initially intended to improve contextual accuracy and reduce hallucinations, appears to have introduced unintended side effects, particularly in edge-case workflows involving structured output generation and file formatting.
Technical analysts note that the symptoms reported by users—such as inconsistent text generation after correction prompts and failure to retain context across iterative requests—mirror known issues observed during previous model transitions. In linguistic and computational pragmatics, context retention and iterative refinement are critical for reliable human-AI interaction. According to linguistic research on conditional logic and user intent, the phrase “if you fix it, then generate” implies a causal dependency that models must interpret correctly. When models fail to maintain this dependency, user trust erodes rapidly. This aligns with findings from studies on human-AI collaboration, where even minor inconsistencies in output reliability lead to significant drops in perceived competence.
Users attempting to automate document pipelines, academic research workflows, and content moderation systems are particularly affected. One developer reported that a script relying on GPT-5.2 to convert scanned PDFs into clean Markdown files now fails in 73% of cases, compared to less than 8% under GPT-4o. “It’s not just wrong—it’s unpredictably wrong,” they wrote. “Sometimes it generates the file with all content. Sometimes it says ‘I can’t do that’ after I’ve already corrected it twice. There’s no pattern.”
OpenAI’s engineering team has acknowledged in internal communications that model stability during incremental updates remains a challenge, especially when deploying new safety filters and alignment techniques concurrently with performance optimizations. While the company has historically prioritized rapid iteration, the current backlash suggests a growing tension between innovation velocity and user reliability expectations.
Meanwhile, users are turning to alternative models—such as Claude 3 and Gemini 1.5—as stopgap solutions. Community moderators on AI-focused subreddits have begun compiling lists of workarounds, including explicit prompting templates and output validation scripts, to mitigate the instability. “You have to treat GPT-5.2 like a temperamental intern,” one user joked. “You give it the task, then you double-check everything, then you do it yourself.”
As the rollout continues, the broader AI community is watching closely. If the instability persists, it could undermine confidence in OpenAI’s ability to deliver on its promise of dependable, enterprise-grade AI tools. For now, users are left in limbo—waiting for a patch, a rollback, or an explanation that has yet to come.
