Gemini 3.1 Pro is officially out, and in this video I walk you through the actual Google blog release plus the public benchmarks that matter if you’re building real systems (not just vibing with hype). I start with what Google is positioning it as: a “smarter model for your most complex tasks,” and honestly that framing is accurate—this one is clearly aimed at situations where a simple answer isn’t enough. Then I cover where you can use it right now: the Gemini API in Google AI Studio (what most of you will use), plus Vertex AI and Gemini Enterprise on the enterprise side, and it’s also landing in the Gemini app and Notebook. From there, I dig into the benchmark tables and compare Gemini 3.1 Pro against models like Claude Sonnet 4.6, Opus 4.6, GPT-5.2, and others across categories like humanities, academic reasoning, and coding-style evaluations. I also point out some of the demos Google showcased—like generating ~500 lines of code for a 3D ISS orbital tracker and improved website generation—which is exactly the kind of practical “can it actually build?” signal I care about. Finally, I break down model specs and pricing: 1M input context, 64K output tokens, January 2025 cutoff, tool use/function calling/structured output, and the big win—pricing stays the same as Gemini 3 Pro ($2–$4 per million tokens on the base tier). Next up, I’m planning a live head-to-head demo versus Sonnet 4.6, Opus 4.6, and a few OpenAI/Grok models for both n8n-style automation use cases and straight coding.