🎥 AI Model Showdown: Gemini 2.5 Pro vs DeepSeek R1 vs o3 vs o4-mini | Radar Chart Comparison Witness a rapid-fire radar chart comparison of top AI models—Gemini 2.5 Pro
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Welcome everyone to this special deep dive brought to you by ATN Newman. We're jumping into the, well, the really fast moving world of AI models. It moves so quickly, doesn't it? Hard to keep up sometimes. Exactly. And, you know, staying informed without getting totally overwhelmed is key. So today we're trying to give you those crucial insights, zeroing in on a...

a really interesting comparison. Yeah, you shared this video and it uses a great visual, this radar chart. It compares four

pretty big names in AI right now. All tested with the same prompt, which is important. Critical for a fair comparison. Yeah. It really lets you see their performance side by side. Okay. So the models we're looking at are Gemini 2.5 Pro, that's Google's, then DeepSeek R1, which is open source, and then OpenAI's latest pair, O3 and the O4 Mini. Quite a mix there. Different approaches, different goals probably. So this radar chart method

You found it pretty effective. Oh, absolutely. It's brilliant for this. You instantly see how they stack up on things like reasoning, language understanding. You get a quick visual benchmark. Really useful. Think of it as your quick...

guide folks to understanding the kind of current lay of the land in AI. Yeah, snapshot. Now, speaking of helpful things, if you are finding this useful, please do take a second to like and subscribe to AI Unraveled over on Apple. It really helps support the show. It does, yeah. We appreciate it. And also, quick shout out to the Jamgatech app. If you're looking to master certifications, up to 50 of them

Using AI, check it out. Links are in the show notes, as always. Definitely worth a look. Okay, so back to this chart. What jumps out immediately when you look at those plotted points? Well, the first thing is how...

how consistent some models are. Each color dot on that chart is a performance trait, right? Right. And when you see those dots forming a tight little bunch, a cluster, it tells you that model performs pretty evenly across different types of tasks. Consistency. And the video showed that for Gemini 2.5 Pro and also DeepSeek R1, didn't it? Yeah. Their patterns looked really uniform. Exactly that. Super uniform.

For the listener, that suggests, you know, a really balanced set of capabilities. You ask it to reason. You ask it to understand language. You kind of get the same level of good performance. Reliable. That's a good word for it. Yeah. Reliable across the board. OK. But then 03 and 04 Mini, they look different, more spread out. Yeah. They showed more, let's say, variation, peaks and valleys in their strength. So what's the takeaway there? Does that mean they're like...

Worse not necessarily worse. No, it just means they might really excel in some areas Maybe even beating these others there, but perhaps aren't quite as strong everywhere else. Okay, it could be a design choice you know focusing on specific skills or maybe it's a trade-off because they're noted as being smaller and Faster models right speed and efficiency. I

But the video did mention they were surprisingly good at real world logic. It did. And that's a key point. It shows that even if the profile is varied, they can still pack a punch where it counts, like in practical problem solving. It really just kind of drives home that idea that there's no single best AI is there. It depends what you need it for. Precisely. Different tools for different jobs.

Okay, before we dig into the specifics of each model's profile, just another quick reminder for everyone listening, like and subscribe on Apple if you enjoy AI Unraveled. And check out that Jamgatech app for AI-powered certification help links in the show notes. Good stuff. All right, so based on that video comparison, what were the sort of defining features for each one? Let's start with Gemini 2.5 Pro. Okay, Gemini 2.5 Pro.

The video really highlighted its strength in multimodal perception. Multimodal, meaning? Meaning it's really good at handling different types of information together. Not just text, but images, maybe audio, video. It understands the information coming from different senses, so to speak. Ah, okay. That makes sense for like...

Analyzing web pages or complex documents with pictures. Exactly. Lots of real world scenarios involve more than just text. And DeepSeek R1, it was tagged as reasoning first and open weight. What's the significance there? So reasoning first suggests its core design really prioritizes logical thinking, problem solving, that kind of heavy lifting. And open weight is huge.

It means the model's parameters, the core parts, are publicly available. So anyone can look inside, basically. Pretty much. It boosts transparency, lets researchers tinker with it, lets developers build specialized things on top of it. It really fuels community involvement. That open aspect is definitely a big deal in the AI space. Okay, then the open AI pair, 03 and 04 mini. Smaller, faster, more

But strong on logic. Yeah, that was the interesting bit. Smaller, quicker, maybe needing less computing power. Potentially cheaper to run then. Potentially, yeah. And despite that varied profile we mentioned, they still showed real capability, especially in that practical, real-world logic area. It proves smaller doesn't always mean less capable, just maybe more specialized or efficient.

So quick recap for everyone. Gemini 2.5 Pro, strong on multimodal stuff. DeepSeek R1 focused on reasoning, plus it's open. In 0304 Mini, the smaller, faster options, surprisingly good at logic, even with varied overall strengths. That's a good summary. And this whole comparison, it just underlines how fast generative AI is moving. And importantly, how many different ways people are building these things. Yeah, different philosophies, consistent all-rounders versus niche specialists.

It's open versus closed. Exactly. Lots of different paths being explored simultaneously. So maybe a final thought for our listeners to chew on. As you hear about these different models and their strengths, what kind of AI profile actually aligns best with what you might need or what you find interesting? Good question. Like, do you need that consistent, reliable performance of a Gemini or DeepSeek?

Or is a model like 03 or 04 Mini maybe super strong in one specific area you care about, more appealing, even with trade-offs elsewhere? Or perhaps that multimodal aspect of Gemini is crucial for your work.

Or maybe the open nature of DeepSeek is what really excites you for building something new. Yeah. Are you looking for the dependable generalist or the specialized, maybe faster powerhouse? It's something worth thinking about as you follow this space, because your answer probably guides which developments you'll want to watch most closely. Excellent point. Well, for more deep dives and AI insights like this, make sure you're tuned in to the AI Unraveled podcast.

And one last time, check out the Jamgat Tech app. All the links you need are right there in the show notes. Thanks for joining us on this exploration. Thanks, everyone.