User Reports Indicate 1.75% LLM Hallucination Rate as Google Prepares for Feature-Rich Gemini 3 Launch

Mountain View, CA – As anticipation builds for the rumored late 2025 release of Google's next-generation large language model (LLM), Gemini 3, a recent study highlights persistent challenges with AI accuracy. A 2025 Scientific Reports study, analyzing three million mobile application reviews, found that approximately 1.75% of user complaints specifically cited "hallucination-like errors" in AI-powered apps. This statistic underscores a key concern among users regarding the reliability of even advanced LLMs.

The upcoming Gemini 3 is expected to introduce significant advancements over its predecessor, Gemini 2.5 Pro, which launched in March 2025. Google CEO Sundar Pichai has confirmed a 2025 release for Gemini 3, with internal leaks suggesting a possible launch as early as October 22. These leaks point to a model with "built-in reasoning that mimics human thought," real-time multimodal understanding, and agentic capabilities.

Industry insiders anticipate Gemini 3 will feature enhanced multimodal processing, supporting real-time video, 3D objects, and geospatial data. It is also rumored to possess advanced reasoning capabilities, potentially integrating a "Deep Think" mode with embedded verifier reasoning to improve accuracy. Furthermore, reports indicate superior coding abilities, with the model capable of generating complex user interfaces from simple prompts and handling intricate SVG files more effectively.

Despite these projected leaps, the practical impact on typical end-users remains a subject of discussion. As one social media user, Haider., noted in a recent tweet, "it's weird how people are so anxious for the Gemini 3 release but it also makes me wonder what this new model will do for the end users that the current Gemini 2.5 pro doesn't." Haider. further speculated, "for most people, it won't meaningfully change their use cases."

This sentiment aligns with ongoing discussions in the AI community about whether incremental improvements in LLMs translate into genuinely transformative experiences for the average consumer. While specialized applications and enterprise solutions may see substantial benefits from Gemini 3's advanced features, the everyday user might find their core interactions largely unchanged.

The issue of AI "making things up sometimes," as highlighted in Haider.'s tweet, continues to be a critical area of focus. Research confirms that even frontier models are susceptible to hallucinations, particularly in complex or multimodal tasks. These errors manifest as factual incorrectness, nonsensical outputs, or fabricated information, significantly eroding user trust. Developers are actively exploring mitigation strategies, such as Retrieval-Augmented Generation (RAG) and Chain-of-Verification, to ground LLM responses in factual data and enhance reliability.