My First Exposure to Gemini 3.1 Pro Model’s Reasoning & Pro plan's 1 Million Token Context Window Impact for Software Development Work

I recently got a feel for the significant improvements that Google AI Pro plan brings for software development work as compared to free tier Gemini/ChatGPT AI. This was in the context of my custom PowerShell script suites which are quite complex. As per Gemini, for an LLM, understanding such custom PowerShell suites is comparable in complexity to understanding medium-complexity Next.js and React Native projects. 

While the Pro plan's 1-million-token context window solved the "sliding window" memory loss issue—allowing the AI to hold sub-folders and many files in its head at once—it was the Gemini 3.1 Pro model’s reasoning power that, as per Gemini, was critical in this PowerShell script suites work. It successfully parsed the complex, custom logic across my interlinked scripts to generate a holistic view and high-quality documentation based on it that less powerful models may have struggled to do.

Gemini 3.1 Pro model seems to be very good at software development tasks as compared to free tier Gemini models I have used in the past.

For more details about my recent exposure, please see my latest posts:

• How AI (LLM) Context Window Size Impacts What Data AI 'Remembers' In Software Development Workflows
• Used Gemini Code Assist in VS Code under Google AI Pro plan to review and greatly enhance documentation of two Powershell scripts repos