Okay, so imagine this AI, right? It just aces complex math, writes code like a total pro, but then ask it to do some, you know, creative design and it just completely bombs. Beat. That's kind of the weird paradox with Grok 4. It really is. A fascinating one. Welcome to the deep dive. Today, we're really digging into Grok 4 XAI's new thing. Just came out, what, July 9th, 2025. Exactly. And yeah, Elon Musk's company, they're saying, you know, world's best AI model. Big
claims. Right. Big claims. You hear that a lot. Cool. We're not just going to take their word for it, obviously. We actually got our hands dirty, ran some real tests, tried to see what's really under the hood. Yeah, we did. So our mission here for you listening is to cut through all that hype. Get to the core of it. Figure out what you really need to know about Grok 4. We'll look at the scale, the benchmarks, some surprises
there. Definitely some surprises. The cost, the economics, and then how you actually use it in a real project. Right, the practical side. That's crucial. Plus, we've got these three key real -world tests. They really show where it works and, well, where it kind of falls flat. Okay, so let's start unpacking this. What is Grok for exactly? The headline number is just huge. Yeah. An estimated 1 .7 trillion parameters. Yeah, 1 .7 trillion. It's hard to even wrap your head
around that number. Think of parameters as like the AI's brain cells or maybe connection points in its network. Okay. More parameters generally mean more power to learn really complex, intricate patterns in the data it's trained on. Right. So how does that compare? Well, for perspective, GPT -4 is estimated around $1 .8 trillion, so kind of similar ballpark there. Google's Gemini Ultra is about $1 trillion, and Anthropix Claude 4 is maybe around $500 billion. So Croc 4 is
definitely up there with the biggest model. So definitely not something you're running locally. Oh, no way. Yeah, you're definitely not running this beast on your gaming PC. It's entirely cloud -based, needs massive infrastructure. Makes sense.
And XAI's claims, I mean, they're bold. better than a phd level in every subject smarter than almost all graduate students in all disciplines simultaneously that's straight from elon musk wow that's quite the statement it is it sets a high bar so boiling it down what's grok 4's biggest raw strength just based on that scale its immense scale allows it to learn incredibly complex detailed patterns stuff smaller models might just miss okay scale is one thing but performance
is another This is where it gets really interesting for me. The actual benchmarks. How did it do? Right, the numbers. On something called Humanities Last Exam, or HLE, it's designed to test broad knowledge and reasoning across different fields. Grok 4 scored 25 % just on its own. But, and this is key, when it could use tools like a search engine, it jumped to 44 .4%. Ah, so it knows how to use tools effectively. That's different
from just raw knowledge. Exactly. It shows it can leverage external resources intelligently, which is, you know, way more like how humans solve problems in the real world. That makes sense. What about more specialized areas? Yeah, for grad level physics and astronomy, it hit 87, 88 percent. That puts it ahead of Google Gemini and Anthropic Glod in those tests. So a really strong grasp of complex scientific concepts. 87, 88 percent on grad level physics. That's
impressive. It really is. But honestly, the part that truly blew me away was the A score, the American Invitational Mathematics Examination. Oh, yeah, that's notoriously difficult. Extremely. Grok 4 scored 95 out of 100. Whoa, wait, 95 percent?
on a 95 that's that's incredible that's not just calculation that's deep mathematical reasoning step -by -step problem solving precisely it really points to that powerful step -by -step thinking capability especially for complex math it's a huge deal imagine an ai solving those multi -step math problems with 95 accuracy that feels like a massive leap and what about for developers coding benchmarks Ah, yes, the Software Engineering
Benchmark, SWE Bench, crucial one. This test is fixing real bugs, adding features and existing code bases. Right, the messy stuff. Exactly. Grok 4 scored between 72 % and 75%. That places it right at the absolute top for tackling these real -world coding challenges. Okay, so putting these numbers together, how do they translate to real -world impact? What's the takeaway? It means elite problem solving ability, especially for tough scientific encoding tasks. OK, those
numbers are seriously impressive. Elite problem solving power. But, you know, power usually comes with a price tag. Let's talk economics. This isn't free, right? Not at all. Grok 4 is a premium commercial product. You access it via an API and you pay for that access. There's no free lunch here. So the value proposition isn't about being the cheapest option out there. Definitely not. It's about being the best for specific high
-value tasks. Think about it this way. Maybe it costs, say, 12 cents for a complex query. Okay. But if that 12 -cent query automates a task that would take a skilled developer, I don't know, hours to figure out, like tracking down a really tricky bug. Right. The return on investment could be massive. Exactly. The cost of the developer's time, the project delays. Suddenly, 12 cents looks incredibly cheap. You're paying for that
elite level performance, that acceleration. So bottom line, is it really worth the cost then? For high value, complex problems, its superior performance absolutely justifies the cost. All right. Makes sense. So let's get practical. How do you actually start using Grok 4 in, say, an automation workflow? What are the paths? There are basically two main ways people are doing it right now. Path one is the direct connection to XAI. Okay, how does that work? It's pretty
straightforward on the surface. You typically use an AI agent node in whatever automation tool you prefer. You grab an API key from the XAI developer console, plug in your credentials. Standard API setup. Right. Then you select the model, which would be Grok 4 -0709 or whatever the latest version is. You can do a quick test, like sending hello, Grok, just to make sure the connection's live. And then you can give it tools,
like web search. Yep. You can add nodes for tools, maybe a propensity node for research or Tavoli or others. Grok 4 is designed to figure out when it needs to use those tools to answer your prompt. Sounds good in theory, but does it always work smoothly? Well... No, not always. We actually tried building something we called an ultimate assistant. The goal was research a topic using two different tools, find a relevant contact person, and then draft an email to them. Pretty
complex task. Multi -step. Yeah, definitely. And we immediately hit a snag. We got this error. Failed to parse tool arguments from chat model. Meaning Grok 4 wasn't sending back its instructions for using the tools in the strict format, the JSON format that the workflow needed. So the workflow just broke. It didn't know what Grok was trying to tell it to do next. Okay. That sounds frustrating. Could you fix it? We tried. We even tried forcing it into a JSON -only output
mode, but that didn't help either. In fact, it just stopped trying to use the tools altogether then. So the direct connection can be a bit finicky. Yeah. I still wrestle with prompt drift myself sometimes, you know, where... The AI's output just changes over time, even with the same prompt. A consistent API is like a godsend when you're building something real. Totally agree. Which leads us nicely to path number two, using OpenRouter. OpenRouter. I've heard of that. It's like a middleman.
Exactly. It acts as an intermediary, a routing layer. And honestly, it's often a smarter and much more reliable way to go. Why is that? What
are the benefits? Several big ones. First. single billing you get one bill even if you use dozens of different models from open ai anthropic google xai whoever that simplifies things a lot okay that's convenient second a unified api format open router makes all these different models talk and basically the same way through their api so your code or workflow setup is much more consistent even if you swap models So less chance of those parsing errors we just talked about.
Yeah, precisely. And third, often the connections just seem more stable and reliable through OpenRouter. In your workflow tool, you'd use a generic chat model node, connect it to OpenRouter, and then just select Zagrok 4 from the list of models they offer. And did that work for the ultimate assistant? Like a charm. It worked perfectly. Grok4, through OpenRouter, laid out its plan.
Research with Perplexity, then research with Tavli, then it synthesized the info from both sources intelligently, looked up the contact, and composed this really well -crafted email, even citing its sources. Wow. So it managed all four tools seamlessly. Seamlessly. It was actually quite impressive to watch it orchestrate the whole thing. So if a developer is starting out, which setup method should they probably prioritize?
Generally, OpenRouter provides more stability, reliability, and honestly, just ease of use, mid -roll sponsor read. Okay, we saw OpenRouter smooth things out. But let's talk brass tacks, speed, and cost for that complex ultimate assistant workflow. How did it actually perform? Right, the performance varied. The first time we ran it, it took about one minute and 40 seconds, which is, you know, pretty reasonable for that complexity. Yeah, not bad. But the second time,
it took over three minutes. Hmm. That's quite a difference. Nearly double the time. Why the variability? Server load, most likely. Grok 4 is new. It's popular. It's powerful. Lots of people are hitting the API. So performance isn't always consistent. That's a really important point if you're building something that needs predictable response times. Absolutely critical. You have to build your applications assuming
that variability might happen. You need error handling, maybe longer timeouts, or ways to manage user expectations if a task takes longer sometimes. It could definitely destabilize things if you don't account for it. Okay. Good warning. And the cost for that run. You mentioned $0 .12 earlier. Yeah, through OpenRouter, that specific workflow costs about $0 .12 each time it ran successfully. $0 .12 doesn't sound like much on its own. It
doesn't. But if you're running that kind of complex workflow hundreds or thousands of times a day, it adds up fast. The cost is driven by the amount of text processed, the input prompt, the tool usage, the back and forth, the final output. They call these tokens. More tokens, more cost. So how do you manage that? You can't just not use it if you need its power, but you don't want costs spiraling. The smart strategy is often a hybrid approach. Use cheaper, faster models
for the initial legwork. Legwork. Maybe use a smaller cloud model like Hypu or one of the smaller Gemini models to do initial data gathering, maybe summarize some long documents or filter information. They're much cheaper and faster per token. OK, so pre -process the information. Exactly. Do the grunt work with the cheaper models. Then take that condensed summary, that key information, and feed only that to Grok for the really high level analysis, the complex reasoning, the final
synthesis, or the difficult coding task. Ah, I see. So you're using Grok 4 strategically, only where its unique power adds the most value, leveraging its strengths without paying for it on simpler tasks. Precisely. That way, you get the best of both worlds. The power of Grok 4 where you need it, but better cost efficiency overall. So how can developers manage these performance and cost variations effectively? Strategically, use cheaper models for initial steps, reserving
Grok 4 for the high -value reasoning parts. All right, we've talked benchmarks. Set up, cost, performance. Now for the really fun part. Putting Grok 4 to the test in some real -world scenarios, we set up three distinct challenges. Yep. Wanted to see how it handled different kinds of tasks you might actually throw at it. Test number one, a simple bug fix. Okay. What was the bug? It was a common front -end issue. A scrolling problem in a React component. We gave Grok 4 the buggy
code and asked it to fix it. It nailed it. In under two minutes, it identified the issue in the CSS, proposed a clean, professional solution, explained why it worked. Pass. Solid pass. Nice. So for straightforward, well -defined, logical problems like fixing a specific bug, it's very effective. Incredibly effective. Really shines there. Okay. Simple bug fix dot check. What about something more complex? Test number two. Complex
feature development. This was ambitious. We asked it to add a memory feature to an existing chat application. Whoa, okay. That's not trivial. What did that involve? It required changes across the board, database schema updates, creating new API endpoints, building new user interface components, modifying the core chat logic to actually use the memory. A lot of moving parts. Yeah, that sounds like a multi -day task for a human developer. Easily. How did Grok 4 do?
Honestly. My mind was truly blown. Really? The entire feature was built, designed, coded, integrated in under five minutes. Under five minutes. Seriously. I didn't write a single line of code myself. I just reviewed what it produced, ran it, and everything worked perfectly on the first try. Wow. Beat. Five minutes. I was trying to process that. Thinking about the usual back and forth, the debugging, the testing for a feature like that. What was the most surprising part for you
just watching that unfold? It felt. Almost like magic. But you could see the logic. It understood the existing code -based structure, which was well -organized. That's important. And it just methodically generated all the necessary pieces and connected them correctly. It wasn't just code generation. It was architectural understanding. It's nothing short of revolutionary for adding complex features to existing well -structured code bases. Well, revolutionary. That's a strong
word. But based on that. Yeah. Okay. Mind officially blown, too. So test one. Simple bug fix. Pass. Test two, complex feature, absolutely revolutionary pass. What was test three? Test three, new project creation. After that stunning success with the feature ad, we thought, okay, let's see if it can build something from scratch. We asked it to create a beautiful landing page for a fictional product. Just from a prompt, make me a beautiful
landing page. Pretty much. We gave it some basic info about the product, but the key instruction was make it beautiful. And the result? Yeah. Was it beautiful? No, not really. The website it generated was functional. The HTML structure was okay. The basic elements were there. But visually, it was really disappointing. How so? The styling was just... Very basic. Kind of bland, even outdated looking. Nothing like what you'd consider a modern, polished, beautiful landing
page. It completely missed the mark on the aesthetics. Ah, back to that paradox we started with. Great at logic and code structure, but struggles with the subjective, creative side. Exactly that. It failed here, we think, because it lacked specific design context or examples in the prompt. And beautiful is just too subjective for it. It doesn't have an inherent sense of visual taste or modern design trends. Right. It needs clear objective parameters or examples for creative tasks. It
can't just intuit good design. Precisely. It struggles immensely with tasks requiring that strong sense of visual design or, you know, open -ended creativity, unless you guide it very, very explicitly. So looking back at these three tests, what's the biggest takeaway? Grok4 excels at structured, logical tasks, even very complex ones, but struggles with open -ended creativity. Okay, we've run the tests, looked at the numbers, the setup, the cost. Let's try to bring it all
together. Is Grok4 worth it? Let's recap the good and the bad. The good. Exceptional reasoning ability, definitely. Powerful math capabilities, as we saw with that aim score. Excellent tool use, especially through something like OpenRouter. And it can produce really high quality research and analysis when guided properly. Okay. Sounds powerful. Right. What about the downsides? The not so good? Speed inconsistency is a big one due to that server load issue. You have to plan
for it. Right. Higher cost, especially if you're using it for high volume tasks without optimization. That direct XAI integration can be finicky as we found. Yeah. The JSON parsing issue. And that
clear struggle with creative. subjective tasks like visual design it's just not its strong suit so the bottom line what's the final verdict look grok 4 is undeniably impressive it's incredibly powerful it excels at complex reasoning math coding within existing structures deep analysis it's not always the fastest it's not the cheapest but when you need that serious intellectual horsepower for your ai automations especially if you're a developer working on well -structured code
or you need deep analysis It's an absolute game changer. It can do things other models just can't or can't do as well. So what defines Grok 4's ideal use case then? It's for serious intellectual horsepower on complex logical problems. So we've really dug deep here, uncovered Grok 4's incredible strengths that logic, the math, the tool use, but also seen its limits clearly. The speed issues, the cost factor that struggle with pure creativity. It's a powerful, almost paradoxical beast, isn't
it? It really is. And this whole deep dive looking at Grok 4, it points to something bigger, I think. A profound shift in how we might approach work, especially complex knowledge work. How so? The future you see feels increasingly agentic. It's becoming less about just how fast you as an individual can code or research or write. And more about. More about how effectively you can direct and orchestrate these powerful AI assistants, maybe even a team of them, to achieve complex strategic
goals. It's like being a conductor rather than just playing one instrument. That's a great analogy. And you're saying this is becoming more accessible? Yeah. The tools are getting easier to use, as we saw with Open Router smoothing things out. The productivity gains, like that five -minute feature build, they're potentially massive and very real. And the price, if used strategically,
can absolutely be justified by the value. So wrapping this up, what does this all mean for you, the listener, the learner, the developer, maybe just the curious mind tuning in? It feels like the landscape is shifting under our feet. It really does. It suggests that... Understanding these tools, learning how to integrate them strategically. It's not just about adding a new skill. It could fundamentally change your capabilities, maybe
10x them, like people often say. The choice seems to be leaning towards embracing these tools, figuring out how to leverage them, or risk getting left behind as others do. That might be the stark reality, yeah. Which leads to a final, maybe provocative thought to leave people with. Go on. How might you listening right now? How might you start to rethink your own role, your own workflow, in a world where AI agents like Grok4 can increasingly accomplish very complex tasks
autonomously? What does that mean for your unique human contribution? That's a deep question. Something to definitely mull over. We hope this deep dive into Grok4 has given you a solid, well -informed starting point for thinking about that. Thanks for joining us. OTRO Music.