Gemini 3 Pro is a groundbreaking AI model from Google that excels in multimodal understanding, coding, and reasoning, marking a significant advancement comparable to GPT-4’s impact. While it offers impressive performance and versatility, it faces challenges with efficiency, cost, and occasional inaccuracies, requiring active supervision for optimal use.
The release of Gemini 3 Pro marks a significant leap in AI capabilities, comparable to the impact GPT-4 had in March 2023. This new model from Google is being hailed as the leader in AI, especially excelling in coding and UI design tasks. The speaker shares their extensive hands-on experience with Gemini 3 Pro, highlighting its impressive performance in benchmarks and day-to-day usage, despite some quirks and occasional odd behaviors. The model’s ability to handle multimodal inputs—such as audio, video, images, and PDFs—without needing separate tools is a standout feature, enabling richer interactions and more versatile applications.
Google’s CEO emphasized the scale and impact of Gemini 3, noting its widespread adoption across Google Cloud and its integration into various products. However, unlike other major AI models that are available across multiple cloud platforms, Gemini models are currently exclusive to Google Cloud, which may limit accessibility for some users. Despite this, the model’s performance on challenging benchmarks like the Humanity’s Last Exam and ARC AGI 2 test is remarkable, showing a significant improvement over previous models and competitors. Its three-dimensional reasoning, demonstrated through Minecraft benchmarks, further showcases its advanced capabilities.
While Gemini 3 Pro excels in many areas, it is not without drawbacks. The model tends to use a large number of tokens, making it less efficient and more expensive to run compared to some competitors like GPT-5.1. The pricing structure also increases sharply for users exceeding certain token limits, which could be a concern for heavy users. Additionally, the model exhibits a relatively high hallucination rate, meaning it sometimes produces incorrect or fabricated information, a problem that other models have started to address more effectively. Despite these issues, Gemini 3 Pro’s speed and writing quality are praised, with the model producing coherent and well-structured text that often surpasses other AI systems.
The speaker also discusses practical experiences using Gemini 3 Pro in coding environments, noting its strong performance in UI generation and tool calling, though it can sometimes get stuck or behave unpredictably. The model requires active supervision and iterative prompting to ensure accuracy and completeness, especially in complex coding tasks. This “babysitting” aspect means that while Gemini 3 Pro is powerful, it is not yet a fully autonomous solution. The community response has been largely positive, with many developers excited about the model’s potential and improvements over previous versions.
In conclusion, Gemini 3 Pro represents a major advancement in AI technology, positioning Google as a leader in the field once again. Its strengths in multimodal understanding, coding, and reasoning are balanced by challenges in efficiency, cost, and occasional instability. The model’s quirks and high hallucination rate suggest there is still room for refinement, but its overall capabilities are impressive and promising. The speaker plans to continue using Gemini 3 Pro extensively and encourages others to explore it, while remaining mindful of its current limitations. This release signals a new era in AI development, with Google making a strong comeback in the competitive landscape.