Google Gemini 3 Pro has been released and is already transforming how financial analysis is done. In a real-world test, I used the new model to answer a critical question: how much is NVIDIA expected to move on its upcoming earnings report? The result? An 8% expected move—based on a blend of historical volatility, standard deviation, and implied volatility. Traditionally, answering such a question would require digging through earnings data, calculating past price movements, and analyzing options data. Now, with Gemini 3 Pro, it takes seconds. I simply asked Aurora, the AI agent powering NexusTrade, to analyze NVIDIA’s earnings history and combine multiple data points into a single forecast. What sets Gemini 3 Pro apart isn’t just speed—it’s accuracy. I tested it against other top models using EvaluateGPT, a rigorous benchmark designed to measure how well large language models generate correct SQL queries for financial analysis. The test included over 90 complex, real-world questions involving multiple joins, time-based aggregations, and statistical calculations. The results were striking. Gemini 3 Pro achieved a one-shot accuracy rate of 88.9%—6.6 percentage points higher than the next best model. It outperformed GPT-5.1, Claude Sonnet 4.5, and even the previous version of Gemini. It was not only more accurate but also more consistent, with a lower standard deviation in results. This means it gets the right answer on the first try, more often than any other model. The model’s ability to understand nuanced financial questions and translate them into precise database queries is a game-changer. It’s not just about retrieving data—it’s about understanding intent. For example, when asked to find the average return and standard deviation of NVIDIA’s stock the day and week after each earnings release, Gemini 3 Pro returned a query that was not only syntactically correct but also semantically accurate. Beyond SQL, I tested its ability to generate visual content. I asked it to create a flow diagram of the EvaluateGPT process. While Claude Sonnet 4.5 provided a text outline, Gemini 3 Pro delivered a fully styled, color-coded, and professional-looking flowchart—exactly as I envisioned. The level of detail and design sense is impressive. This isn’t just about better tools—it’s about a shift in the role of the financial analyst. The real value is no longer in writing code or running spreadsheets. It’s in asking the right questions, framing the problem correctly, and interpreting the output. The model does the heavy lifting. With Gemini 3 Pro now integrated into NexusTrade, I can automate entire research workflows. I can analyze earnings expectations, build trading strategies, and test hypotheses—all without writing a single line of code. For investors, this means faster, smarter decisions. For the industry, it means a new era of efficiency. The days of manual data crunching are fading. The future of investing is here—powered by AI that doesn’t just assist, but truly understands.