👉Gemini 2.5 Pro vs DeepSeek R1 vs o3 vs o4-mini
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Welcome to a new deep dive from AI Unraveled. This is the podcast created and produced by Etienne Newman, who's a senior software engineer

and also a passionate soccer dad up in Canada. Great to be here. And hey, if you're finding these explorations into AI useful, please do take a second to like and subscribe on Apple Podcasts. It really helps us out. Definitely. So today we're doing something a bit different, a rapid visual cour, you could say, across the, well, the cutting edge of AI. Mm-hmm.

We're looking at a comparison of four pretty fascinating models. Yeah, exactly. We're digging into this really interesting snapshot. It comes from a radar chart comparison, and it features Gemini 2.5 Pro, DeepSeek R1, and then OpenAI's O3 and O4 Mini. So it's a good chance to get a quick handle on how they all stack up against each other. And this comparison, we spotted it from a Reddit post by Onomen, is really compelling because it's so visual, right? You see this chart, this radar chart.

And almost instantly, you kind of get a feel for each model's strengths across different areas like reasoning, language understanding. Yeah, it maps it out clearly. So our mission today basically is to really dig into what this visual benchmark is telling us about these leading AI players and their unique capabilities. And what's really interesting here, I think, is that it wasn't just like a theoretical thing. The source material, it mentions that these four models were actually given the exact same prompt.

They tackled it in a real-time reasoning test. Okay. So while the Reddit post itself focuses mainly on the chart, the result, knowing there is this practical challenge behind it, adds, well, another layer of interest, doesn't it? Yeah, absolutely. It grounds it in reality. Okay, so let's get into the specifics then. The models themselves. The post we're looking at, it highlights what makes each one a bit different. Gemini 2.5 Pro, for instance. We're told it really emphasizes multimodal perception.

Now, for someone maybe just tuning in, what's a good tangible example of where that multimodal strength would really shine? Okay. Well, think about maybe a situation where an AI needs to understand a complex social media post.

It's often not just the words, the text. There might be a photo or a video with it conveying, I don't know, emotion or some extra context. Got it. So a model with strong multimodal perception, like Gemini 2.5 Pro is supposed to have, could process and importantly integrate both the text and the visual information to get a much richer, more accurate understanding of the whole message, the sentiment, everything. That makes sense. It sees the whole picture, not just parts.

Okay. Then there's DeepSeq R1 described as prioritizing reasoning and also being an open weight model. Yes. That reasoning first part sounds, well, pretty crucial for tackling complex problems. What sort of tasks would a model like that really excel at? Well, a reasoning first approach usually suggests that its core design, its training is heavily focused on logical inference, problem solving, that kind of thing.

OK. So this could mean it performs better in tasks needing step-by-step thinking. Things like debugging code, maybe planning complex projects with lots of dependencies. Right. Intricate stuff. Exactly. Or even just answering, you know, complex logical questions accurately. And the other part of DeepSeek R1's description, the open weight bit. Why is that significant in the AI world? Oh, yeah. That's actually a really important distinction.

Open weight means the model's learned parameters, basically. The core of its knowledge, how it makes decisions, are made publicly available. Oh, interesting. It's huge for transparency, really. It lets researchers, the whole AI community, basically, look under the hood. They can scrutinize it, understand how it works, maybe even build on it or tweak it for specific uses. It's a level of access you don't always get with proprietary models. Right, like seeing the AI's engine, as you said. Fascinating.

Okay, now let's shift to OpenAI's 03 and 04 Mini.

The description says they're smaller, faster, but surprisingly capable in real-world logic. What kind of real-world logic are we talking about? And why are speed and size such key factors here? Good questions. So real-world logic generally means the sort of common sense reasoning we humans use all the time, almost without thinking. Like, you know, knowing if you drop a glass, it's probably going to break. Or understanding simple cause and effect. Basic stuff, but clear.

Crucial. Got it. Common sense AI. Pretty much. And for models like 03 and 04 Mini, being smaller and faster is a big advantage. It makes them potentially really useful for applications where you need quick answers, maybe on devices with less power, like your phone. Or for handling lots of requests quickly. Exactly. High volume situations. So the fact they can still handle this kind of everyday logic efficiently, despite being smaller, is, yeah, quite impressive. Okay, so let's bring it back to the radar chart.

visualizing all this, the post mentions each colored dot on the chart is a performance trait. Right. So when we look at where those dots fall for each model, what are the key things to like pay attention to? Well, the main thing is how far each dot is from the center. The further out a dot goes, the better the model performed on that specific measure, that trait. Higher score, further out. Exactly. But just as important is the overall shape or pattern the dots make for each model. Oh, so? If you're

If you see a tight cluster of dots for one model sort of close together, it suggests that model performs pretty consistently across those different tasks being measured. A balanced performance. You could say that, yeah. Which leads us to a really interesting observation from the Reddit post. Which was? It mentioned that both Gemini 2.5 Pro and DeepSeaCar 1 showed, and I quote, remarkable uniformity in their performance profiles on this chart. Uniformity. So like you said, balanced performance.

What does that consistency across the board suggest about their overall abilities? Well, it points towards a sort of well-rounded competence. These models probably don't have glaring weak spots, at least not in the areas tested by this benchmark. They seem to perform reliably well across different kinds of challenges. Which could be a big plus for general purpose applications. Absolutely. A significant advantage if you need a broad set of skills. Okay.

But then conversely, the post points out that 03 and 04 mini showed more varied strength profiles, more uneven patterns on the chart. What can we take away from that? A more varied profile. Yeah, it suggests these smaller models might really shine in certain specific areas. Ritualisms. Exactly. While perhaps being less strong in others. Right.

It points towards a more specialized toolkit, if you like. So one might be great at language, less so at math or something. Potentially, yes, or vice versa. This kind of specialization can make them incredibly useful for very specific, targeted jobs where their particular strengths are exactly what's needed. Hmm. Interesting tradeoffs. This whole rapid visual benchmarking idea is

It really seems like a valuable tool for, well, anyone involved in AI, right? Definitely. The post mentions developers choosing models, researchers tracking progress, enthusiasts just staying informed. Why is this quick visual comparison so effective for all these different people? Well, think about it. For a developer, it's like an instant overview. And at a glance, look at how models compare on key things, making that tricky choice of which model to use maybe a bit easier, more efficient. Saves waiting through tons of specs.

Right. And for researchers, these visual benchmarks help spot trends quickly, identify areas needing more study, compare different approaches. Yeah. See the landscape shift. And for anyone just fascinated by AI, it's just an accessible way to grasp the relative strengths of these top models without needing a Ph.D. in machine learning. It really is like a cheat sheet for the current AI state of play.

And speaking of staying ahead in this fast moving tech world, if you or anyone listening wants to get a serious edge,

Maybe master some in-demand professional certifications. Always a good idea. You really should check out Etienne's AI-powered JamGat app. It's designed specifically to help you prep for and pass, I think it's over 50 different certifications now. Feels like cloud, finance, cybersecurity, healthcare, business, loads of them. That's quite a range. Yeah, it is. We'll put the links for the JamGat app in the show notes, of course. Definitely worth a look if you're boosting your skills. Sounds useful. Okay, first of all,

Bringing it all back together from this radar chart comparison, what are the main takeaways? Well, it seems Gemini 2.5 Pro and DeepSeek R1 come across as pretty consistent, well-rounded performers across the board based on this test. Uniform. Yeah. And you have O3 and a 4 Mini, smaller, faster, but showing these more distinct, varied profiles suggests maybe more specialized strength. Right.

Different shapes on the radar. Exactly. And the chart itself, it's just a really effective way to quickly visualize and benchmark these models as they keep evolving so fast. It absolutely provides an insightful snapshot. And looking at these different strengths.

You know, Gemini's broad abilities, DeepSeek's reasoning focus, the speed and maybe specialized logic of the smaller models. It does make you wonder, doesn't it? Wonder what? Well, which of these characteristics, multimodality, reasoning first, speed, efficiency, specialization, which ones might end up being the most crucial, the most transformative for the AI applications we'll see next? That's a big question. It is. And what does that varied performance of O3 and O4 Mini tell us?

Does it hint at a future where maybe we see more specialized, highly efficient AI systems becoming more common for specific tasks? It's definitely possible. A trend towards specialization alongside the big general models. Yeah, it's definitely some food for thought. And on that note, listeners, don't forget to explore Etienne's AI-powered Jamgatak app if you are serious about mastering those critical professional certifications. Cloud, finance, cybersecurity, healthcare, business,

All those areas. Check the show notes for the links. Exactly. Links are right there in the show notes. Thanks, everyone, for taking this deep dive with us today.