My First Impressions of Gemini 2.5 Pro

I've been using Gemini 2.5 Pro for just a short while now, but it easily feels like months. That's the funny thing about habits: the truly transformative ones integrate so quickly, you forget what life was like before.

That's how it feels having left the landscape of previous LLMs after years inside what now feels like a more constrained environment. It wasn't a difficult switch, technically, but adjusting my workflow took a moment. Since then, it's consolidated into one massive, ongoing chat containing thousands of lines of code, ideas, and reflections – my primary interface for creative and technical problem-solving.

It taps into that fundamental human capacity to adapt rapidly to powerful new capabilities. I find myself relying on its unique ability to tackle problems that stumped other models, its knack for creativity, and its skill in connecting disparate concepts. The raw numbers back up my subjective experience too – Gemini 2.5 Pro leads benchmarks like GPQA (science questions) and AIME (math problems) without needing specialized test-time techniques. It's not just marketing – there's a qualitative leap in how it approaches complex tasks.

The benchmarks are impressive enough to mention specifically. Gemini 2.5 Pro has claimed the top spot on LMArena by a significant margin, outperforming both Claude 3.7 and GPT models. On technical benchmarks, it's reached 63.8% accuracy on SWE bench for coding tasks, and an impressive 18.8% on Humanity's Last Exam – a challenging benchmark created by hundreds of subject matter experts to test the limits of AI reasoning.

Maybe it takes more conscious effort to switch tools as you gain experience, resisting the comfort of the familiar. But the reward here is rediscovering the sheer joy of leveraging a tool that expands your own potential – the speed, the intelligence, the massive 1M token context window allowing for unprecedented depth. And that context window is no gimmick – I've watched it analyze entire codebases (100,000+ lines) in a single session, maintaining coherence throughout. Where other models fragment understanding across multiple chats, Gemini can hold the entire conversation history plus documentation, making problem-solving feel genuinely cumulative rather than disjointed.

What most impresses me is how this translates to real-world coding tasks. Recently, I fed it a complex problem involving a web app with multiple interconnected components. With other models, I'd carefully fragment the task into digestible chunks. With Gemini, I dumped the entire problem statement, existing code, and documentation into one prompt. It not only understood the full context but developed a solution that elegantly connected all components – something that would have required multiple back-and-forth exchanges with other models. The "thinking" capability it employs (where it essentially fact-checks itself during generation) has virtually eliminated the "confidently wrong" responses that plague most LLMs.

I'm genuinely excited about this shift, but I also know that tool choices are deeply personal. There's no single 'best' for everyone. Others have workflows, existing integrations, or simply haven't felt the need to explore this particular option yet, and that's perfectly valid. I still use Mistral Small, Hermez 3.5 or o3-mini for my workflows.

Experience teaches that sharing genuine enthusiasm tends to pique curiosity more effectively than trying to force a conclusion. My approach is simply to relay my positive experience, much like the encouraging feedback I've seen shared by others within the Cursor community.

Think of it as just putting my observations out there. Like leaving a signpost for fellow travelers who might be curious about this path.

Ultimately, my motivation is driven by the potential I see and the results I'm getting, regardless of broader trends. I'm particularly eager to see the more agentic tools that will inevitably be built on this powerful foundation. Imagine tools that can not only understand complex codebases but autonomously debug issues, propose optimizations, and even implement them with minimal oversight. We're seeing early glimpses of this with Devin and Claude Code such as in GitHub issue resolution demos, where the model can ingest entire repositories, locate bugs, and generate fixes.

It's like shifting from a basic toolkit to a state-of-the-art workshop, ready to build bigger and better things. It's difficult to imagine reverting; the expanded capacity for creative problem-solving feels like a significant, and very welcome, leap forward.