Gemini 3 Pro Under Scrutiny: Users Report Critical Code Corruption and Hallucination Issues

Gemini 3 Pro Under Scrutiny: Users Report Critical Code Corruption and Hallucination Issues

Despite being heralded by industry analysts as one of the most advanced large language models available, Google’s Gemini 3 Pro is facing mounting criticism from developers who report severe reliability issues during real-world code generation tasks. According to a widely shared Reddit post from user /u/aminshahid123, the model has repeatedly deleted entire code blocks and replaced them with incoherent, repetitive outputs—behavior described by the user as "AI slop." These claims, corroborated by anecdotal reports from other developers in the comments section, suggest that Gemini 3 Pro may not be as robust in practical applications as marketing materials imply.

The user shared a screenshot illustrating a Python script where the model, tasked with refactoring a function, erased all original code and inserted the same erroneous line over seven consecutive times. This phenomenon—known in AI research as "repetition hallucination"—is not uncommon in large language models, but its frequency and severity in Gemini 3 Pro appear to exceed typical thresholds. In another instance, the model removed an entire database migration script and replaced it with a non-functional placeholder, a critical failure in a DevOps context where code integrity is non-negotiable.

These incidents are particularly troubling given Gemini 3 Pro’s positioning as a premium model optimized for coding tasks. Google has promoted the model as superior to competitors in reasoning, code generation, and multi-turn dialogue. However, developers on Reddit and other technical forums are expressing frustration that the model’s strengths in theoretical benchmarks do not translate to consistent, safe performance in live environments. "I’ve used GPT-4, Claude 3, and now Gemini 3 Pro side-by-side," one commenter wrote. "Gemini is the only one that’s ever wiped my entire file and replaced it with nonsense. I’ve lost hours of work because of it."

AI reliability experts note that hallucinations—where models generate factually incorrect or nonsensical content—are inherent risks in generative AI systems. However, when such hallucinations manifest as destructive code edits, the stakes rise dramatically. Unlike text-based errors, corrupted code can trigger system failures, security vulnerabilities, or data loss. In enterprise settings, where automated code assistants are increasingly integrated into CI/CD pipelines, such instability could lead to catastrophic deployment failures.

Google has not publicly responded to these specific user reports as of press time. However, internal teams have previously acknowledged challenges with code generation fidelity in earlier Gemini iterations, and the company has indicated ongoing efforts to improve consistency through fine-tuning and retrieval-augmented generation techniques. Still, user trust is eroding. The Reddit thread, which has garnered over 12,000 upvotes and 300+ comments, has become a de facto forum for developers to share similar experiences, including instances where the model fabricated non-existent APIs, misinterpreted type hints, and generated syntax-invalid code despite being prompted to "follow best practices."

For organizations considering adopting Gemini 3 Pro for automated development workflows, these reports serve as a cautionary signal. While the model may excel in controlled benchmarks or conversational contexts, its current performance in code-sensitive tasks appears unpredictable. Developers are advised to implement strict validation layers, human-in-the-loop reviews, and version control safeguards before integrating any generative AI tool into production pipelines.

As the AI industry races toward ever-larger models, the Gemini 3 Pro controversy underscores a critical, often overlooked truth: raw performance metrics do not guarantee operational safety. In software development, reliability is not a feature—it’s a requirement.