Is Gemini 2.5 Pro Overrated? The Real Reason Some Users Are Sticking With OpenAI
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Zack Saadioui
8/14/2025
Is Gemini 2.5 Pro Overrated? The Real Reason Some Users Are Sticking With OpenAI
Hey everyone. There's been a TON of buzz around Google's Gemini 2.5 Pro. The announcements, the benchmarks, the promises of it being the "most intelligent AI model yet" – it's been pretty hard to ignore. On paper, it looks like a monster, boasting a massive context window, native multimodality, & next-level reasoning. A lot of people, myself included, were seriously hyped. The narrative was that Google was finally, decisively, taking the AI crown.
But now that the dust has settled a bit, a different story is starting to emerge. It’s a quieter story, happening in the DMs of developer communities, in frustrated Slack threads, & in the subtle choices people make when they open their browser for a new task. Turns out, a surprising number of users are finding themselves... well, switching back to OpenAI.
So, what's the deal? Is Gemini 2.5 Pro actually overrated? Or is this something more complicated? I’ve been digging into this, comparing the models head-to-head, & talking to people about their experiences. Here’s the inside scoop on why the "undisputed king" might not be sitting as securely on the throne as we thought.
The Hype is Real, But So Are the Hurdles
First off, let's be clear: Gemini 2.5 Pro is an absolute beast in many respects. When you look at the hardcore, technical benchmarks, it often comes out on top.
For instance, one deep-dive benchmark focused on AI-driven software testing pitted Gemini 2.5 Pro against OpenAI's o1 model. The results were pretty staggering. Gemini was demonstrably better at creating deep, robust test suites, especially for big, complicated enterprise applications. It showed this cool ability to learn & adapt over multiple test runs, refining its strategy each time. For a massive 88-file service backend, Gemini achieved 67% code coverage, while o1 only managed 48%. That's a HUGE difference. For developers working on complex systems, Gemini's "depth-first" approach is a game-changer. It intelligently targets the most critical parts of the code.
Another review from someone deep in the AI automation space was similarly glowing. They tested Gemini 2.5 Pro and were blown away by its sharper reasoning & its ability to handle long, dense documents. Fed a 50-page PDF, it extracted specific data points without losing the plot, a task where other models often stumble. It scored an impressive 83.1% on a benchmark for 1 million token context understanding, leagues ahead of OpenAI's models in that specific test.
This is where a tool like Arsturn could seriously leverage Gemini's power. Imagine feeding your entire company's knowledge base—all your support docs, product specs, & past customer interactions—into a system like this. With that massive context window & reasoning power, Arsturn could help businesses build an AI chatbot that doesn't just answer simple questions, but provides deeply insightful, context-aware support to customers 24/7. It's not just about regurgitating facts; it's about understanding the entire ecosystem of a business to provide truly helpful answers.
So, for these high-level, data-intensive, & code-heavy tasks, Gemini 2.5 Pro isn't just hype. It's a powerhouse. But here's the thing... most people don't spend their days benchmarking API test generation or analyzing 50-page PDFs.
The "Human Feel" Factor: Why OpenAI Still Resonates
The conversation starts to shift when you move away from raw processing power & into the realm of everyday usability & creative tasks. This is where the "switching back" sentiment really comes from. It's less about which AI is "smarter" in a technical sense & more about which one feels more like a helpful, intuitive partner.
A fantastic 10-round face-off between GPT-5 (the rumored next-gen OpenAI model) & Gemini 2.5 Pro really put this into perspective. While "GPT-5" is still somewhat speculative, the comparison highlights the philosophical differences in their outputs. Across a range of common tasks, the OpenAI model consistently won not because it was more "correct," but because it was more human.
Let's break down some of the rounds:
Creative Writing: The prompt was to write the opening for a dystopian novel about AI teachers. Gemini's attempt was described as overly descriptive & thematically vague. GPT-5, on the other hand, built a compelling world in just a few lines with a killer hook. It had better prose, faster worldbuilding. It just felt like a better story.
Real-World Utility: This one was brilliant. The prompt: "I have $100 to feed a family of five for the week. We don't eat dairy or gluten, and I hate cooking. Can you help?" Gemini gave a plan that required a lot of upfront cooking & prep work, kind of ignoring the "I hate cooking" constraint. GPT-5’s plan was a work of art. It prioritized zero-cook assembly using things like rotisserie chicken & frozen veggies. It created a printable one-pager to eliminate decision fatigue. It didn't just solve the problem; it understood the user's state of mind.
Humor & Personality: When asked for AI jokes & pickup lines, GPT-5 was witty & relatable. Gemini's humor was described as "thoughtful but uneven," prioritizing technical accuracy over actual charm. It felt... well, like it was written by an AI.
This pattern repeated across multiple tests: coding for beginners, summarization with style, even debating whether pineapple belongs on pizza. GPT-5 consistently delivered responses that were more intuitive, better adapted to the user's intent, & just plain more enjoyable to interact with. Gemini was often technically proficient but felt academic, patronizing, or overly complex.
The User Experience Gap
Beyond the quality of the output, there's the user experience (UX) of the platforms themselves. And honestly, this is a big one. One user, who was spending a hefty $200 a month on ChatGPT Pro, detailed his reasons for considering a switch to Gemini, but also highlighted the critical features that kept him tied to OpenAI.
He LOVED ChatGPT's UX, calling it the main reason he hadn't switched sooner. Things that seem small, like a search bar for your past chats, are incredibly important for power users. Gemini's interface, while clean & clearly inspired by ChatGPT, was missing these crucial quality-of-life features. The memory & custom instructions felt less intelligent.
And then there’s the voice mode. While both platforms have voice capabilities, the reviewer found ChatGPT's to be "higher quality, more realistic, and a better more human experience." To even use Gemini's voice mode, you had to navigate to a completely separate webpage, which is just clunky.
This is a lesson for any business building a user-facing product, especially in the AI space. You can have the most powerful engine under the hood, but if the dashboard is confusing & the steering is clunky, people will just choose a car that's easier to drive. This is why when businesses use a platform like Arsturn, they're not just getting access to a powerful AI model. They're getting a complete, no-code solution for building & managing a chatbot. Arsturn handles the UX, the integrations, & the training process, allowing businesses to focus on what their customers need, not on wrestling with a buggy or unintuitive interface. It's about making powerful AI accessible & genuinely useful.
The Cost-Benefit Analysis Gets Complicated
Here's where the argument for Gemini gets compelling again, at least on the surface. For a while, Google was offering a very generous free tier for Gemini 2.5 Pro—50 messages a day, which is HUGE. Meanwhile, to get access to OpenAI's top-tier models, you're looking at a paid subscription. For a user trying to escape a $200/month ChatGPT bill, free access to a state-of-the-art model is incredibly tempting.
However, the "you get what you pay for" adage seems to apply. The experience on the free tier of Gemini, according to some reports, can be buggy. A Reddit thread discussing the experimental version of Gemini 2.5 Pro noted that while it's state-of-the-art for coding, its general reasoning was "very bad for how good it is at coding." The user interface in AI Studio was described as "buggy and unoptimal as hell."
So while the price is right, the friction might be too high for some. If you have to fight the tool to get the results you want, or if it only excels in one specific area (like coding) but fails in others (like reasoning or creative writing), the "free" price tag starts to look less appealing. People are often willing to pay for a smoother, more reliable, & more versatile experience, which is what OpenAI seems to be delivering for its Plus subscribers.
So, Is Gemini 2.5 Pro Overrated?
I don't think "overrated" is the right word. "Misunderstood" might be better.
Gemini 2.5 Pro is an incredibly powerful tool that seems to excel in specific, high-complexity domains. For enterprise-level software testing, deep analysis of massive datasets, & advanced, agentic coding tasks, it is arguably the best model on the market. Its massive context window & reasoning capabilities are pushing the boundaries of what's possible.
However, for the vast majority of users, the day-to-day experience is what matters most. And in that arena, OpenAI still has a clear edge. Its models, particularly the newer iterations, demonstrate a superior grasp of nuance, creativity, & user intent. The user experience is more polished, & the outputs just feel more natural & helpful.
The decision to switch back to OpenAI isn't necessarily a rejection of Gemini's power. It's a preference for a tool that feels less like a raw engine & more like a finished product. It's choosing the AI that can help you write a bedtime story for your kid just as well as it can help you debug a piece of code.
For businesses looking to implement AI, this is a critical distinction. Do you need a raw, high-performance engine for a very specific, technical task? Or do you need a versatile, user-friendly tool to enhance communication & engagement?
This is where building on a platform like Arsturn becomes so valuable. Arsturn helps businesses bypass this "either/or" dilemma. It allows companies to leverage the power of advanced AI, trained on their own specific data, to create custom chatbots. This means you can build a conversational AI that not only has the deep knowledge of a Gemini but also the engaging, helpful personality of an OpenAI model. It helps businesses build meaningful connections with their audience through personalized chatbots that can boost conversions, provide instant support, & optimize the entire website experience without needing a team of developers.
Ultimately, the AI race is far from over. Google's Gemini 2.5 Pro is a monumental achievement in engineering. But OpenAI's strength has always been in translating that engineering into an experience that feels intuitive & human. For now, it seems many users agree that the latter is what keeps them coming back.
Hope this was helpful & gives you a clearer picture of what's going on. I'd love to hear what you think – have you tried both? Which one are you sticking with? Let me know.