Google dropped Gemini 2.5 last week and it's not just another incremental update. This is the first time one of their models has actually topped the benchmarks that matter, beating OpenAI's o3-mini and DeepSeek R1 at reasoning tasks. More importantly, they're claiming all future models will have reasoning capabilities baked in.
I've been testing it for the past few days and the difference from previous versions is night and day. This feels like Google finally figured out what made OpenAI's o1 series special and said "we can do that too, but better."
What's Different About Reasoning Models?
Traditional AI models generate answers pretty much instantly. They're fast but sometimes shallow—they're pattern matching more than actually thinking through problems step by step.
Reasoning models take longer but work through problems more methodically. They consider multiple approaches, catch their own mistakes, and arrive at more reliable answers. It's the difference between a snap judgment and careful deliberation.
Gemini 2.5 Pro Experimental (that's the full name, catchy right?) leads LMArena benchmarks over o3-mini, Claude 3.5 Sonnet, and DeepSeek R1. These aren't made-up metrics—this is based on real-world tasks from users comparing models blind.
I Threw Hard Problems At It
First test: a complex coding challenge I use for interviews. Most AI models either fail or give solutions that technically work but are inefficient. Gemini 2.5 not only solved it but provided three different approaches with trade-offs explained.
The thinking process was visible too—I could see it considering edge cases, catching potential bugs in its own code, and refining the solution. That transparency matters. When an AI is making decisions for you, knowing how it got there is crucial.
Second test: ambiguous business strategy questions. The kind where there's no right answer, just trade-offs. Asked it to analyze whether a startup should expand to international markets or focus on domestic growth first.
It laid out assumptions, considered multiple scenarios, identified dependencies and risks, and gave a nuanced answer that acknowledged uncertainty. That's reasoning, not regurgitation.
Someone I know who teaches physics used it to check homework problems. Said it not only got the answers right but showed work in a way that helped students understand where they went wrong—better than previous models that would just give correct answers without explanation.
The Token Budget Thing Is Clever
Here's a neat feature: you can control how much the model "thinks" before answering. It's called thinking budgets. More tokens means more deliberation but higher cost and slower responses. Less tokens means faster but potentially less thorough answers.
For simple queries, dial it down. For complex analysis where accuracy matters, crank it up. I've been using low budgets for basic coding questions and high budgets for architecture decisions. Works well.
This addresses one criticism of reasoning models—they're expensive to run. With budgets, you're not wasting resources on overthinking simple problems.
Where It Still Struggles
Creative tasks aren't its strength. Asked it to write a short story and while the plot was coherent, it felt mechanical. Reasoning models optimize for correctness and logical flow. That's great for problem-solving, not so great for creative writing.
The speed is also an issue. Even with thinking budgets dialed down, it's slower than standard models. For quick queries where you just need a fast answer, GPT-4o or regular Gemini 2.0 Flash are still better choices.
And weirdly, it can overthink simple things. Asked it what 2+2 equals and I swear it spent 5 seconds considering the question from multiple angles before confidently saying 4. Sometimes you just want a fast answer, not a philosophical treatise.
The Competitive Picture Gets Interesting
OpenAI's o1 series was impressive but expensive and not widely available. DeepSeek R1 was the efficient open-source alternative. Now Google has a reasoning model that's topping benchmarks and available to everyone with a Google account (though with usage limits on the free tier).
This is good for consumers—competition means better products and lower prices. But it's also raising questions about how sustainable this is. These models cost a fortune to run. Google, OpenAI, and others are burning money to gain market share.
Anthropic's staying out of the reasoning model race for now, focusing on reliability and safety with Claude. That might be smart—let others battle it out while they corner the "AI you can trust for serious work" market.
The "All Future Models" Claim
What really caught my attention was Google's CTO saying all future AI models will have reasoning capabilities built in. Not as a separate thing like OpenAI's o-series—just as a core feature.
That makes sense. Once you figure out how to do it efficiently, why wouldn't you bake it in? It's like how spell check used to be a separate tool but now it's just expected in any text editor.
If Google pulls this off, it shifts the baseline for what "good AI" means. Other companies will have to match or risk looking outdated.
Real-World Applications I'm Excited About
Medical diagnosis support could be huge. Reasoning through symptoms, considering rare conditions, and explaining the logic? That's exactly what this is good at.
Legal research and contract analysis. Lawyers spend hours reasoning through case law and precedents. An AI that can do that reliably would be massively valuable.
Engineering and architecture, where you need to consider multiple constraints and trade-offs. The ability to think through complex problems step-by-step translates directly to value.
Data analysis where you need to understand not just what the numbers show but why and what it means. Current AI is okay at the first part, reasoning models could nail the second part too.
My Honest Take
Gemini 2.5 feels like Google finally nailed what they've been trying to build for two years. It's not just competitive—in many ways it's leading. The combination of strong reasoning, multimodal capabilities, and that massive context window makes it formidable.
I'm not switching everything to Gemini 2.5, but I am using it for complex problems where I need reliable, well-reasoned answers. For quick queries and creative work, I'm still bouncing between ChatGPT and Claude depending on the task.
The broader trend is clear though: reasoning capabilities are becoming table stakes. Within a year, having an AI model that can't think through problems step-by-step will feel quaint.
Are we getting closer to AGI? Probably not as fast as some people claim. But we're definitely getting AI that's more reliable and useful for complex real-world tasks. That's progress worth paying attention to.
If you haven't tried Gemini 2.5 yet, give it a shot with something hard. Not "write me an email" but "help me think through this complex decision with competing priorities." That's where you'll see the difference.
The AI race just got more interesting. And this time, Google's not coming in third.