How Google Quietly Took the Lead in the AI Race with Gemini 2.5

This model isn’t just smart on paper

IQ Test Results

Something feels different this time—it doesn't seem like just another failed launch from Google. I don’t want to downplay the past efforts of the DeepMind team, but the truth is, they didn’t always meet user expectations.

Just a few weeks ago, Google released Gemini 2.5 Pro, and the internet lit up. Maybe not quite as explosively as with DeepSeek or GPT-4o, but still, I have to admit that ever since Google launched DeepResearch, the updates that have followed have been impressive—definitely worth noting.

In a lot of my early tests using prompts, the results were surprisingly strong. Naturally, it was hard not to compare it to other AIs out there.

One example that really stood out to me came from a hospital in Japan, where the same AI technology was used to transcribe and summarize doctors' notes, cutting down nurses’ paperwork time by 42% and helping to ease their stress.

And in lab evaluations, Gemini 2.5 has been able to solve PhD-level science and math problems that had stumped earlier models.

Google has always been in the AI race—often in the background and sometimes underestimated. But now, it seems like their moment has finally arrived.

That said, beyond my own thoughts—or any excitement I might feel about this new model—we should take a closer look at whether Gemini 2.5 truly lives up to the hype. More importantly, can it become something we actually use in our daily lives? In other words, can it do more than just draft an email or suggest a good restaurant?

Outpacing GPT-4 and Claude: What Sets Gemini 2.5 Apart?

Google’s Gemini family of models was introduced as a direct answer to GPT-4, and the 2.5 Pro version takes that competition to a new level.

What makes Gemini 2.5 Pro stand out is its ability to break down problems in depth, rather than simply repeating training data. Google describes it as a “thinking model,” built to reason through challenges step by step before delivering a final answer.

“Unlike GPT-4 and Claude 3, which generate responses based on pattern recognition, Gemini 2.5 claims to methodically ‘think’ through problems before replying,” one analysis notes.

In real-world benchmarks, Gemini 2.5 Pro outperformed GPT-4, Anthropic’s Claude, and other leading models in areas like programming, math, and science—ranking at the top in evaluations such as GPQA.

Another area where Gemini 2.5 Pro leads the pack is memory.

GPT-4’s longest context window tops out around 128,000 tokens, while Claude 3 reaches approximately 200,000. Gemini 2.5 pushes far beyond both, with an impressive one-million-token context window—and plans to double that to two million.

In practical terms, this means it can handle full books, entire codebases, or large datasets without losing the thread of the conversation. There’s no longer a need to break up input or keep reminding the AI what was said 20 messages ago—Gemini maintains full context throughout.

In one demo, Gemini 2.5 successfully analyzed a 500-page AI index report, cross-referencing charts across different pages to answer a complex question.

At its core, Gemini 2.5 is designed to be multimodal. While GPT-4 and other models often rely on separate systems to handle different types of input, Gemini 2.5 Pro can process text, images, audio, video, and even programming code—all within a single unified model.

By comparison, OpenAI’s GPT-4 has limited image understanding via a plugin and offloads image generation to a separate model (DALL·E).

That said, OpenAI and Anthropic aren’t staying idle—GPT-4 has rolled out improvements like GPT-4 Turbo, and Claude 3 has also expanded its context window and capabilities.

Still, as of early 2025, Gemini 2.5 Pro has a strong case as the most advanced model available. It debuted at number one on the LM Arena leaderboard by a wide margin.

Gemini’s strength lies in handling complex, intellectually demanding tasks—reasoning through multi-step problems, working with code, and managing multimodal input with ease.

Gemini 2.5 in Action

All the benchmark achievements in the world wouldn’t matter if Gemini 2.5 Pro couldn’t tackle real-world problems—or at least come close.

In corporate offices and content studios, Gemini 2.5 is already proving to be a valuable tool. At FOX Sports, for example, the team turned to Gemini to help them sort through a massive and ever-growing video archive—nearly 2 million clips—to find key highlights or specific moments. What used to be a slow, manual task is now as simple as typing a natural language query. Thanks to Gemini’s ability to understand both the content and context of video, staff can instantly retrieve the exact footage they need.

In the advertising world, agency WPP used Gemini to generate campaign content. The AI was trained on WPP’s brand guidelines—tone, color palette, typography, and past campaign examples—and tasked with drafting social media ads.

The results?
Gemini was able to write ad copy and even generate sample visuals that matched the brand’s identity. The content looked and sounded like WPP, all with minimal human input. Early feedback suggests the agency was able to scale personalized campaigns for different audiences significantly faster than usual.

Developers are also putting Gemini 2.5 Pro to work—not just for prototyping, but in real production settings.

In one case, a logistics company integrated Gemini into its route optimization software, asking the model to intelligently reroute delivery trucks. The pilot, run in March 2025, was a major success: the company saw a 15% drop in fuel consumption, a 22% increase in on-time deliveries, and projected annual savings of $3.5 million after applying Gemini’s route suggestions.

Other developers have used Gemini to build full web app prototypes from just a short description. According to one AI expert, people are already using it to “create complete web apps with a single prompt.” It’s also being used to build games, design websites, write marketing content, and automate data workflows—just by describing the desired outcome in plain language.

The academic and scientific research communities are also seeing Gemini’s potential. Google has developed a tool called Gemini Deep Research, powered by the 2.5 Pro model, that can scan the web and academic databases to compile comprehensive research reports on a given topic.

With strong performance on scientific quality benchmarks—and even a high score on a daunting test known as “The Final Exam of Humanity”—Gemini 2.5 is showing that it’s more than just a programming assistant or chatbot. It’s shaping up to be a tool that can help generate new insights, pushing it closer to becoming a true research partner.

Final Thoughts

Based on everything we've covered—and factoring in the latest IQ test rankings in the current AI race—Google is, for now, in the lead with Gemini 2.5. But the story is far from finished.

It’s worth noting that even Gemini’s own creators continue to frame it as a tool meant to enhance human capabilities, not replace them (even if that message feels familiar by now).

The real challenge lies in how we integrate such a powerful technology into society’s most important systems—like education, research, and ethical decision-making.

In the end, the most important answers won’t come from Gemini itself, but from the choices we make as a society in this new era of AI-driven innovation. The spark has been lit—what we do with it next is up to us.