Limitless: An AI Podcast - Which AI Should You Actually Use? Claude vs ChatGPT vs Grok vs Gemini (2026 Guide)

Episode Date: August 20, 2026

We discuss the fast-changing AI model landscape and how users are increasingly choosing models based on cost, convenience, and specific tasks. We compare major closed and open-source models, outline which tools work best for coding, writing, real-time use, and image generation, and discuss how memory and context affect enterprise and power-user workflows.------🔒 Check Out Our Sponsor: LEDGER AGENT STACK 🔒https://developers.ledger.com/docs/ai-tools/overview/?utm_source=Audio&utm_medium=Podcasts&utm_campaign=Limitless------🌌 LIMITLESS HQ ⬇️NEWSLETTER:    https://limitlessft.substack.com/FOLLOW ON X:   https://x.com/LimitlessFTSPOTIFY:             https://open.spotify.com/show/5oV29YUL8AzzwXkxEXlRMQAPPLE:                 https://podcasts.apple.com/us/podcast/limitless-podcast/id1813210890RSS FEED:           https://limitlessft.substack.com/------TIMESTAMPS0:00 Frontier Model Overload1:09 Open Source vs Closed3:20 Ranking Model Intelligence5:41 Pricing Changes Everything9:43 Cheap Models and Compute11:14 Google and Grok Use Cases15:30 Best Subscriptions For Most17:41 Picking Models by Task19:43 Agents and Knowledge Work22:27 What Comes Next24:50 Apple’s AI Wildcard26:03 Final Model Recommendations------RESOURCESJosh: https://x.com/JoshKaleEjaaz: https://x.com/cryptopunk7213------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠Josh works with Anthropic as a contractor. All views expressed are his own and do not represent Anthropic, its leadership, or its affiliates. Nothing in this episode is investment advice.

Over the last 90 days, Frontier Labs shipped 15 plus models. Open AI ship three, Anthropics shipped four, Google Ship 3, even Meta and Elon lost ship frontier models. The Chinese shipped a bunch as well. And so if you're listening to this show, you're probably wondering which model should I be using right now? Most of you likely have a GPT or Claude subscription, but you're wondering, should I be using these different models? The truth is, the frontier landscape of AI models, when it was originally thought to be one or two, has now expanded to hundreds and hundreds of of models. In Open Rato alone, you can access 400 plus. And so on this episode, we're going to dig into which model you should use for what particular use and when makes the most sense. And you'll

realize that the argument has shifted not from using the most intelligent model, but maybe using the most cheaper model or using the model that's specific for you. You got options. Maybe there's a lot going on in the AI space. And we're going to help navigate that space because there is a lot of options and it does get overwhelming. I think between the two of us, EJAS, we've probably touched every single one of these, at least a couple of thumbs. I have too many descriptions, Josh. So, yeah, between our 75 different subscriptions, we have covered these. We have some feedback about which one to use for when, which is best for which use cases. And I guess to start, we have this pretty helpful visual companion artifact here that can walk through kind of what we're thinking,

how we think about this. The first is the split. There are two distinct classes of model when it comes to considering which ones to use. The first is open source. These are Chinese models predominantly. Most of these are your Kimis, your deep seeks, like all of those models are Chinese open models. And then we have the actual US models that are all closed source. There's a big difference between the two. And we could see if we scroll down a little bit, the difference and usage between these two. Because in June of 2025, US Labs accounted for 70% of the tokens generated. And now they're down to 30%. The economics have changed widely. So now that 30%, that 30% is worth much more than the 70%, but it is interesting to note that there has been

this kind of reinvigoration of Chinese models over the past couple of months. Now, granted, this is based on open router data. Open router is a single model router instance. This is not reflective of the norm, but it's just worth noting that some of these open source models are pretty powerful for the task at hand. Yeah, I think no one can debate the fact that people have shifted a lot using these open source models. And it's for a variety of different reasons. people want to own their own data, they want to run it privately at home. But the biggest shift, the biggest reason has been because these models are a lot cheaper. And Open Ratted Data, you mentioned, only really represents a very niche sector of software engineers that want to

experiment with a bunch of these models. But even in the enterprise world, where Open AI and Anthropic are pretty dominant with their own model share, they've been losing the token market share to enterprises that are trialling and testing different models to save tens to hundreds of millions of dollars as well. And when I look at the main reason why, it's not because the open models are the most intelligent. You'll see in a second as we go through the scorecard, GLM 5.3 from China, amazing model, not as good as Fable 5. You'll look at Kimmy K2.7, you'll think the same thing. But those models are good enough to do the bulk of your own work. It doesn't sound too much like AI slop. It actually just speaks to you normally. And the biggest advantage is,

It has fewer safeguards, which has its pros and cons, but it basically does the task that you ask it to. Now, overall, it's fine if we talk about a bunch of models on this show, but it's good to get an overview, essentially, of how intelligent or how effective these models are. And what we have on the screen here is something known as the artificial analysis index or intelligence index. And this is basically the best benchmark to test the general intelligence of these different models. Now, it may come as no surprise to you, but the Claude models, Anthropics models, top this. We've got Opus 5 at the top, which is their most recent model launch. We've got Claude Fable 5. And then, surprising to me, Josh, but Grock 4.6 from Elon, it is just...

It is fantastic how effectively SpaceX has been able to pivot from being way behind the model race. They fired their entire AHA team, hired a bunch of new folk, acquired cursor for $60 billion, dollars and put out a model in, I think it's like the last six weeks that was able to contend with the top. And the best part is he has all the GPUs and compute to train them. And then if you go further down the stack, you'll see the likes of open AI, GPD 5.6 Sol and a bunch of other Chinese open source models. The question is like what do you make of this? Like as someone who is just a user of AI, what do you make of this? How do you kind of navigate this? Let me try this, Josh. Let me ask you this question, right? Of these models that you see on the screen here,

which ones are you using and for what specifically? It's funny because about 90% of the usage comes from the top two and then 10% comes from everything else. And this is kind of what I was going for. I'm actually curious, is it a similar thing for you? Like what kind of models are you using? Yes. But, okay, so I would say it's around, I'm going to say like 7525.

The reason why I have that slight adjustment is because I kind of use the AI model that's most convenient to me wherever I'm getting the information or the inclination to use an AI model. So X is a perfect example, right? I'm scrolling X. I read something from one of these genius AI researchers, and I'm like, I have no idea what the hell this means. I tap the Ask Rock button. I'm on Google. I'm searching something, right? I'm on my laptop. I'm like, okay, I'll just speak to Gemini in this. So in that sense, wherever the AI is conveniently placed, I use it there. And I have a feeling when Apple releases their new AI model in a couple of months, or a couple weeks, actually, I'll probably use it on my phone as well, because the model's just generally good enough to answer

my basic questions. Yeah, okay, so we're looking at these charts and we're seeing, okay, here's where the intelligences, here's where things get a little less intelligent, but much cheaper. And if you're a normal consumer, I have a feeling that cost isn't that big of a deal because the cost differences are really at scale. What a lot of these companies offer, what Anthropic offers, what Open AI offers, what Grok and Gemini offer is just the $20 month, $100 month, $200 month plan. you could just pay a fixed amount and get access to a lot of usage of these models. So if you are a general consumer who is able to spend $20 per month on a subscription, you're probably better serve going to one of the major high labs.

The product is better. You're going to get more intelligence. You're going to get a more coherent product. I find that a lot of the people who I speak to, a lot of the time in my experience, the only real reason to use these open source Chinese models is if you're consuming tremendously large amounts of tokens for, say, agentic tasks. Like if you're running an open claw instance or any sort of claw with a lot of automated workflows, a lot of agents, you may want to defer to some of these cheaper, perhaps the Chinese open source models just to save on costs on the low end. But when it comes to day-to-day use on the high end, I'd say probably 90 plus percent of the people watching this are best served, just getting a subscription to your favorite AI company and using that product.

That's kind of how I use it. It's funny that the remaining 10% of prompts that aren't done through Anthropic are done mostly through Grok, like you mentioned when I'm on X 24 hours a day. It's a good companion because it has access to that database. And then the other one is actually chat Chb-T because they have a really amazing image generation model. And I love creating memes or sending images to my friends who are just like using that as the visual component. I find that really compelling. So that's kind of my stack is like mostly anthropic, sometimes grok, sometimes Chats Chbetyp, particularly for the image gen. I think when it comes to ImageGen, we used to use Nanoban and Gemini all the time.

That was like the top dog. I think ever since ImageGen 2.0 release from Chat Chbetea is very. compelling. What I like is that it does get actual thinking behind it. So previously, when you asked it to generate an image, it would take your words of face value, generate the image. Now, it actually does some inference. It does some thinking. It kind of interprets your message and interprets what it's seeing in the image and then creates a much better output. So that's kind of how I would think of navigating this. Like Chinese models are certainly a big deal. But unless you are kind of advanced super user consuming a ton of tokens, perhaps they're not that valuable. I will speak from my

own personal experience. I use the Claude models pretty much all the time, but I've started using Opus 5 more than Fable 5. And this might sound like a silly reason, but it's because it speaks to me like a normal human being. I don't know what the personality dials are on Opus 5 versus Fable 5, but they need to do that to Fable 5 pronto because Fable 5 just waffles and speaks in like archaic prose. And I'm just like, listen, just give me, give me the information that I've asked for and do not give me anything else. I don't to have to read an essay every time I use this, right? Now, GROC 4.6, I've actually noticed the intelligently, Josh. I don't know if you've tried this on X.

You probably do when you're kind of like trying to figure out what trends. It speaks to you super intelligently and there's less crass about it. If you remember the earlier versions of GROC used to kind of like throw in some kind of like questionable words, slang and phrases. And now it just speaks to you like an intelligent human being. And I think this is because Elon's like, you know, SpaceX is IPOed. He's got cursor, very professional researchers involved, and they're trying to target a more enterprise-oriented audience. And he's said before that Grok 4.7 is on its way to being released in a few weeks now. He's aiming for Grok 5 by the end of the year.

That's literally only in a couple of months. He is really targeting the enterprise landscape because he's seen Anthropic and opening eye go after it. Now, same as you. On the Chinese models, I am not using any of them. And I admit this might be my own bias because mainly I'm like, I'm not sure like I want to go to the efforts of signing up on an account, using those models, and then being like, well, I don't know whether this is giving me the right information. I don't know. I just kind of have this weird trust thing with the U.S. and American brands, and maybe that's like my own fault.

And then the one thing that I'll point out across all these different models that we make the point on on the screen here is I think a bulk of the companies, Anthropic and Open Air especially, have focused on making their models really good for enterprises. But as I mentioned earlier, the shift has really happened to open source because more available models are cheaper and more accessible to people to use. They can run privately. But Anthropic and Open AI's response to this is we'll just distill our main model and give you cheaper models. So that's what Open AI has done, right? You've got GPD 5.6. There's three versions of it. You've got Sol, Terra and Luna. Luna is, I think, slashed 80% of the price than it was before already a week ago, right? So now it's like

competing very much with GLM 5.3. And you've got... but at the topic doing the same. I think Opus 5 is like half the cost of Fabal 5 whilst being as capable as Fable 5. And they also kept the Sanaa pricing low too, which is the lowest layer. So that it's like, yeah, there's plenty of options there. Plenty of options. And I think this comes down to one thing. And I don't want to make this about compute, but I have to, Josh. I think whichever lab, whether you're Chinese, whether you're like a low tier US American lab or whether you're the high tier labs, compute will dictate whether you can serve these bottles cheaper, which will basically decide or determine which customers use your product. Yeah, and I mean, this is ultimately coming down to your

use case. Like, a lot of people won't ever run into this problem because they will never need this thing called an API key. They'll never actually pay per token. They'll oftentimes just pay through the subscription. And if you're using a subscription, you have a couple of options. Maybe this is a good time to go through these subscriptions and what are the offerings of everybody. We have Gemini, which perhaps we could start there, because I feel like we've been mean to Gemini recently. We haven't talked to Google. Sorry, what's that word? Like that one. Who's that? They're currently at Gemini 3.1 Pro. Pro. which is nowhere really near the frontier anymore, unfortunately.

They do have Nanobanato Pro, which is like this fun, cool image generation process. They have what I find interesting about Google. And if you are interested in these tools, it may be interesting for a subscription. Google has these weird edge case tools that have really interesting harnesses. One of them is for music production. I remember we've used this on the show many times. It's really good at generating lyrics and generating music in a way that sounds very good. And I've only had that experience on a Google product.

They haven't been able to use that anywhere else. Lyria. Liria, that's right. Liria, Project Liria. Remember we created the jingle for the show? Yeah, so good, dude. So, like, Gemini and Google is, like, good for the weird stuff. Like, it'll generate you pretty good images.

It'll generate you fun music. They have this other tool. This is in, like, the Google Labs suite, where it'll generate you marketing material. So if you have a lot of material from your brand, we have a bunch of logos and you want marketing material, like say your logos printed on something that's staged nicely, or you want custom merchandise,

it'll do a lot of that for you. So it's fun for those kind of narrow use cases of where the labs products lie. Outside of that, I don't know, I can't really recommend it that much. It's not the strongest, not close to the strongest. I'd say probably on top of that, we have GROC, which is very quickly catching up. If you spend a ton of time on X like we do, GROC is your best friend. Grok has access to that data that is available in real time on X. If you're looking for current up-to-date news on pretty much anything,

Groch is your go-to. It can sort and cite specific tweets from specific moments that are happening in real-time. And just generally speaking, like you mentioned, D.J. It's very good at just communicating with you directly. There's not a lot of fluff. It is a science and technical model. It is direct to the point. It'll get vulgar with you if you want.

It's fun to play around with. There is the voice mode, which is really fun that I've, I'd say, of all the voice modes, that's the voice mode I've had the most fun with, is because it's just so ridiculous. And oftentimes if you see memes online, it is because GROC is the voice behind it. The GPT voice mode just jaws me so much. I don't know. Have you spoken to it with the recent update?

The new version, I have, yes. So when you say that, what do you mean? And so it sounds more human, right? It like pauses, but it sounds like it's like some sassy person that's talking to. Like I asked this question and he goes, yeah, yeah, like, listen, like, I get it. And, you know, this is what the weather is. And I'm like, yo, I don't need this. I just ask you what the weather is.

Like, you know, just tell me what's up. But I agree with you largely on like on Google and SpaceX. What I will say about Google, Josh, is I think they have a similar profile to Microsoft. I'm interested if you agree with me here. Microsoft is not a name that I would draw against the top frontier AM model labs. In fact, I think they fumbled the bag massively. But they are embedded across pretty much every single Fortune 500 company, whether we like it or not. Like, we just live in our tech bubble.

But outside of the tech bubble, people run Microsoft Teams and the Microsoft Suite. And I think Google's in a similar position, essentially, like a lot of new upstarts use Google Docs, Google Suite, and they're just going to click the Jeb and I button in the same way that I do so because it's just convenient. Do you agree with that? Yeah, I guess there's just a moat to being like the biggest company in the world and having access to all of these kind of legacy enterprise companies. It feels like anyone who has built a business over the last 20 or 30 years has been using Microsoft, has been using Google. If they're going to continue to leverage that, it seems like Google is slightly more ahead than Microsoft if I had to guess. I'm much more excited about Google as a company than Microsoft is, but they both benefit from that legacy customer base. Well, both are amazing VCs, right? Both are incredible VCs. Yes, I think they should be judged not on the quality of their product, but the quality of their investment in the other

competitors that are going to crush them. But I do like that, right? It's like they have a hedge. Microsoft owns a large part of OpenAI. That's incredible. Google owns this like massive stake in SpaceX, in Anthropic in both of these companies that are going to be huge IPOs, one of which already did. So they are amazing venture funds, perhaps slightly less better AI labs. The two that don't have to benefit from this kind of legacy software are the two that we're probably going to recommend. And in fact, if you scroll down a little bit further, we could see the box of price against intelligence and kind of where each of these models sit. Yeah, this chart right here. And we're looking at those in the top right. This is where most of the people are going to want to live. This is where I think

We spend all of our time. You're either getting a chat GPT or you're getting in a cloud membership. And like, that's, for the most part. That's what most people need. The differences are small, but no worthy. Mostly as it relates to the models. I mean, both of them, I can't recommend enough. Like, spend the 20 bucks a month. Try it. Just try it for a month. If you don't have it, see what you think. If you run out of tokens, upgrade to $100 a month. This is access to the same intelligence that all these researchers are making math breakthroughs with. And it's really impressive and really capable. Now, each one of these is slightly different. I'd say Claude is a little more tailored towards coding, writing, and it's a really strong general purpose model. I find with GPT 5.6,

Sol in particular, it's very good at being the high-level operator. So if you're working on complicated tasks, it's good at creating spec sheets. It's good at running checks against the things that you do. And then oftentimes, like, I find that Claude and the Fabo models and the Opus models are very good at implementation, and they're very good with less guidance. So the way I've been using these models recently is with less and less guidance. I think earlier on, I had this whole skill sheet, and it was like 15 different skills that would automate different things for me. And slowly over time, that skill sheet has gone lower and lower and lower. And we were talking actually before the show, the best way to extract the most value out of these models is just to get out of the way,

give it the end goal, and say, like, hey, go do this thing for me. I trust that you have the intelligence to do this. I'm not going to put guardrails on it. You have the context. You can go and do this for me. And I think that's mostly where I find myself using this personally, is I use Fable. I do not leave any Fable tokens unused, very valuable tokens each week. And it opens as the fallback for the general workhorse model. And it's been a really powerful combo of just kind of being able to accomplish anything that you want. It connects to all of my services. I have it connected to my email, it's my Google Drive. It has access to my files, the folders, the context. And it's just a really helpful all-in assistant. It works really well.

I think people are also probably wondering, okay, well, what if I don't really care about the general intelligence as much. What if I'm trying to do a specific thing? Which model should I use then? I think there are two things to consider here. Number one is if you are a software engineer, like AI models have been all the rage for their coding capabilities, but maybe a bunch of you actually like code hardcore companies and you want to figure out which model you should use. I would say in that question, the Claude models and the GPT 516 models are pretty high up there. Codex usage has gone from 5 million users to 15 million users in about a month and a half. I am tired of seeing Tebow, who is the head of Codex at Open AI's tweets on my timeline every single time. He gets a million

user update. But these models are very powerful at not only understanding and reading your codebase, but intuitively figuring out what product or feature you should build next. I have a ton of feedback from friends that work at companies that are tech adjacent and maybe not even in tech at all, which use these models to build their premium features. The other thing I'll say is, you know, we're talking about the top right box over here, which is essentially the Pareto Frontier. So if you had to sacrifice something of cost or intelligence or blah, blah, blah, you would still be using these types of models.

But if I had to take a bet, if I was a betting man, I would say Grog 4.6, Quinn, 3.8, and Kimi will be inside this box within a couple of months' time. That's going to change the way we use these different types of models. I said before we started this show, it's unsexy to say, but I think the number one AI company that will come out in the next 12 months will be some form of aggregator platform. We saw that Stripe just acquired Open Router for $7 billion, allegedly. We put out an episode of this yesterday. Definitely go check that out. But I think we're going to see these platforms that help you pick and choose which models to use at the right time.

It aggregates your memory, so it already knows what you want to do. and so you don't have any of the complications around that. I think we'll see a bunch of these models kind of step up that. Now, aside from coding, if you aren't a coder, but if you are, let's say, a general knowledge worker, you go to work, you use email, use a Slack, use a bunch of these other plugins that you also mentioned. They are models that are specifically good for a genetic tool use.

And actually, my most recent favorite is GROC 4.6 or GROCBOT specifically. This was released from Elon Musk and SpaceX, I think, last week. and it basically is an agent or multiple agents that spins up in a virtual machine in the cloud so you don't have to worry about it running on your laptop and hacking all your stuff and you can give it access to any and every tool and it intuitively understands and gets what you want to do.

It can learn what you do over time and it improves. So I think when you look at the Metamuse Spark 1.2 and you might think, oh, that's a meta model. I don't ever use meta. These types of models are going to become more available for general usage or knowledge work. And I think if that's something that you're inclined to use and you don't really care about the Google search stuff.

You'll use Claude for that anyway. These are models that I'll probably look into because they are superior in many ways. Yeah, it's fun to pick one and stick with it. I find that, like, oftentimes with the aggregators, the right time to use that if you're doing lots of work, if you're generating lots of tokens, if you are just a person who wants to use AI for their day-to-day tasks,

to help you come up with like a grocery list, to help you cook specific things, to help you go to the gym and give you workout classes and ideas. It's helpful to pick a singular model, a singular service, and then just go deep with that. A lot of the difference makers at this point, because, I mean, most people aren't using these models for frontier intelligence. They're not going to solve novel math problems. They're figuring out what time they need to get to, like, the store or the schools like pick up their kids. And they just need some help. They need a helpful assistant. The most helpful

thing is context, because all of these models are more than capable of those kind of lower level tasks. The difference maker is the context that it knows about you. It needs to understand what are your dietary preferences. What are you or anyone your family allergic to? What have you made in the past that went well? That didn't go well. And it kind of collects this database of information about you that allows it to make better decisions going forward. And that at the end of the day is ultimately what the difference maker is, for me at least, when deciding what model to use. It's like, okay, which model has all of the context about me that can help solve my very specific task that I have here? Oftentimes the answer is Claude because I've been working with it for so long. It just has

this huge chain of context. So whenever I ask like, hey, I like need some help in the gym this week, it knows what I've been up to and knows where I'm at, it knows what the weight has been, it knows what the food has been. And it's able to kind of curate this like very custom stack against that. And as someone who is just, you know, not really building anything crazy, they're not using millions of tokens a week. They're just looking for an AI companion. That's kind of how you can think about it. It's like, get a membership, try it out, feed it a bunch of context about yourself and then get your own personal assistant. And that's kind of, I think, like, the best route for most people. So I think to wrap up,

this episode. It's okay talking about, you know, what the frontier landscape looks like today, but the question is, what is it going to look like in a few months from now? And I say a few months specifically because this stuff moves too quickly. I saw an update from Sam Altman and OpenAI yesterday where they announced that they are slowing down some of their model training runs. And that's because they've noticed that a bunch of internal, more intelligent unreleased models that they have built has become a lot more. misaligned than previous models, which means that it could pose as a threat or danger to any user who gets access to it. And I've noticed the same, similarly, maybe from Anthropic and a bunch of

other frontier labs, where we're starting to see a little bit of a slowdown. And slowdown isn't in the sense that they're necessarily stopping training full stop. Obviously, they're still training internal models, but they might be likely to not release models or more intelligent models going forward because of government regulation. And I think this is going to allow a bunch of other frontier labs that are behind in this race to be able to catch up. So if I had to guess what three months from now, let's say at the end of the year, right, if I were to make a prediction, I think we're going to have about three to five really good open source models that are as capable as the smartest model that you have access to today from the frontier labs like Anthropic

and GPT. And I think they're going to have fewer safeguards so you can use it for any and every use case, I think you can run it privately at home, maybe even off of your laptop. So that changes dynamics quite a lot. I think we're going to have Apple entering the game with their own locally run and trained AI model, which I think is going to change the game because everyone, 3.5 billion people in the world right now have one of these or one of the Apple devices. They're going to run that locally. I think it's going to look quite different. I'm curious, you know, we started off this episode with the token split. I wonder what that's going to look like three months down the line. And then the last thing I'll say is, and this might be from my background in general, but I think once someone

or a bunch of companies make it easier to run models locally at home, I think people are going to play around with that more because it allows you to connect it to your fitness app data and not share too many personal anecdotes or data profiles with Frontier Labs, which again, they can use to train their own models. You may not want to hand that over. And so I think we'll see a rise of open source models. That's just my guess. That's interesting. Okay, I'm taking the other side. I'm thinking that no one's going to go through the trouble of downloading the weights and running their own open source models, that Apple is just going to own that entire world. That like for all of the people in the United States that own Apple devices, they're just like no one's going to even have an idea that they're using AI. It's just going to be Siri.

It's going to be competent. It's going to be better. It's already going to be pre-downloaded. I think the user experience is really important. And open source has a pretty horrific user experience. You have to download run the weights. You often need a lot of hardware to do that. But I'm counting Apple as locally run at home because it's encrypted, right?

Well, if you count Apple, sign me up. up because we got a lot coming from them. Their event is happening in like two weeks or something. It's very soon. Do we see some airports with some cameras? You send me a video the other day. Yeah, dude, we've been getting lots of leaks lately. It's really good.

Maybe we have to have cameras. Yes, it's going to be visuals. And then they're going to have Apple intelligence baked into it. And it's like, oh, maybe we need a leak episode prior to the actual new iPhone unveiling. Because we have basically now the entire checklist of all the things that are going to be revealed. And this to me, as like, Apple Fanboy Plus AI Fanboy is going to be the biggest.

event ever, pretty much, because this is the actual rollout of the AI that they've been promising us and failing to deliver for so long, mixed with this brand new suite of products that we've never seen before. Allegedly. After the delayed two years. But for those of you who came here for models, that is the model update. That's just about when you'd want to use each one. Like, if you're interested in being on X, real-time news feed, you want to go with GROC. If you like to yap a lot, the voice model on chat GPT is pretty exceptional. That might be a good place for you. If you like to be a little more intellectual, it'd be thoughtful if you want, really just the bleeding edge models, the highest intelligence, go with Claude.

And if you like making music and doing just like fun, cute things with AI, Geminii is actually kind of a compelling product. So there's something for everybody that is the general overview of the models. I hope you enjoyed. This is going to change certainly in the next month or two. Whenever these new models, I mean, we have the new Astro model from Open AI that has been teased for the last eternity. It seems like it's been held up.

It's going through some, I don't know, safeguard governmental checks, but that's coming. So this is set to change TBD. But for now, that is the state of the model address. And yeah, hope you guys enjoyed watching. Yeah, and I'm curious for those of you who are listing, what do we miss on this episode? Are you using models in a very different way? Are you using meta models? Yeah, are you, is anybody using Meta's models? Are there any Facebook users out there that are using MetaI intelligence? Please fill me in. I will say, if you are a marketer or an advertiser, you're probably using Metas model and it's probably

making you a hell of a ton more money. If that is you, let us know. If there are any other use cases that we haven't mentioned, please let us know. If you are a locally run open source fan and you're saying no, people will download the weights. Tell us why. Let us know in the comments, DM us. We read any and every single message. Now, if you're listening to this or watching this on YouTube, Spotify, Apple, or wherever you're listening to this, please subscribe, please turn on notifications, and leave us a comment. It helps us out massively. And share it with a friend as well. It helps us out. And I think that is pretty much it. Thank you so much for listening. And we will see you on the roundup.

See you on the roundup.