ChatGPT vs Claude vs Perplexity: Which AI engine powers your business best?

Your team is probably using AI wrong, and it's costing you time, money, and results. That's the reality Alane and Micah see with most business owners who think "AI" means just ChatGPT—when there's actually a whole garage of different engines to choose from.

In this episode of Automate Your Agency, they break down their favorite car analogy that finally makes AI model selection crystal clear. You'll discover why using GPT-4 for simple tasks is like driving a V12 to the grocery store—overkill and expensive—while using the wrong model for complex work leaves you stranded.

They dive deep into the real differences between ChatGPT, Claude, and Perplexity, sharing exactly when to use each one for maximum impact. From Claude's deep research capabilities to Perplexity's live web data and GPT's versatility, you'll learn how to match the right AI engine to your specific business needs.

In this episode, you'll discover:

• Why your team gets inconsistent AI results (hint: it's not just prompting)
• The exact models Alane and Micah use for different business tasks
• How to mix and match AI models within automation workflows
• Real examples like pulling Google reviews with location-specific searches
• When to stop doing manual AI work and start automating it
• The critical difference between training data vs. live web search

This isn't about picking one AI and sticking with it—that's leaving money on the table. Smart business owners test drive different models and use the right tool for each job. If your team is frustrated with AI results or you're overpaying for simple tasks, this episode gives you the framework to optimize your AI strategy immediately.

Ready to implement smarter AI workflows? Grab our 25 AI prompts guide with department-specific use cases by requesting it here.

Disclosure: Some of the links above are affiliate links. This means that at no additional cost to you, we may earn a commission if you click through and make a purchase. Thank you for supporting the podcast!

For more information, visit our website at biggestgoal.ai.

Want more valuable content and helpful tips?

Explore our Courses/Programs:

• Complete our self-paced Process Mapping course

Enjoy our Free Tools:

• Free Asana, ClickUp, or Monday.com Selector Tool
• Get 25 Free Custom Automation Ideas for your Business

Connect with Us:

• Join our Community
• Follow us on Instagram
• Follow us on LinkedIn
• Subscribe to our Youtube channel

Transcript

0:00:00 - (Alane): Welcome to Automate Your Agency. Every week we bring you expert insights, practical tips and success stories that will help you streamline your business operations and boost your growth. Let's get started on your journey to more efficient and scalable operations. All right, Micah, I thought this episode would be great to talk about the different AIs, because either we get questions about which ChatGPT or Claude or which one to use, or there's an impression that maybe there's only one to use.

0:00:33 - (Alane): And so I love your car analogy that you've created to explain the different models, the different AIs, and you know, just to kind of start to frame what we're talking about with AI.

0:00:46 - (Micah): Yeah, this, this absolutely is my favorite analogy for this because it fits so well. So if we think about CHAT GPT, I think a lot of people and a lot of people that we talk to go was chatting with Chat GPT. But that ChatGPT is kind of like the, the make of like a Ford or a Chevy. Right. So we've got ChatGPT, but under the hood we have different models which would be engines of a car. Oh, for example, in a car you would have a four cylinder, you got a V12, you have your electric motors under GPT, you have, you know, 4.1 GPT, 4 GPT, 3.5, an older model.

0:01:26 - (Micah): And so those are what power GPT. And depending on which model you select, it's different. Just like different engines in a car. Right. You could use a V12 for your grocery getter, but is that the best use? Some people would argue absolutely.

0:01:45 - (Alane): Right, right.

0:01:46 - (Micah): Others would be like, no, that's way too much gas to be. And so it's too expensive. AI models are exactly the same way. If you use a super powerful model for simple things, you're essentially using way too much gas. You've got a gas guzzler here that's possibly eating up a lot of costs when you could have a very simple model like GPT 4.1 Mini, which would be a much smaller and less expensive model to get the job done.

0:02:20 - (Alane): And some of the models too, even there's like a little description of what that model is good at. So using the right model for spreadsheets and data versus another one so that you're getting the best results for the inputs that you're giving. So paying attention to which model slash engine that you're using. And so with the makes like Chevy or Jeep, then we've also got, we've got chatgpt like you talked about. But then there's some other ones like Perplexity and Claude, and who creates it as Anthropic. So there's like a lot of words too, that it makes it really muddled on.

0:02:57 - (Alane): OpenAI owns ChatGPT, Anthropic owns Claude. And so there's these extra layers of things too.

0:03:05 - (Micah): Yeah. And really, who owns it is not. Not super important when it comes to deciding, unless you have personal preference, why you're going one over the other. But definitely, definitely, you have Claude that has separate models. So you have Cloud Sonnet, which is probably the most popular. You have Claude Opus, which is technically supposed to be a stronger model than Claude Sonnet, but it tends to get used less and it has less things said about it.

0:03:38 - (Micah): And then over on the GPT side, right, you have GPT 4.1. We're waiting for GPT 5 to come out. You have.03. You have all these different options to choose from within those. And so when the key to all of this is experimentation, much like taking a car for a test drive, you're going to run it through and you're thinking about the specific purpose. Right. If you're a fleet manager, you're looking for a utilitarian car that's going to get in and fulfill the specific functions that you need. You might not want all the niceties, but for your car that you drive daily, you might want really great suspension, you might want power, everything.

0:04:20 - (Micah): You might want the electronic dashboard. You have different uses for that. You need to test drive the different models to make sure that it's working and going to work for what you're looking for. AI is exactly the same way. So when people come to us and they say, well, my team's using ChatGPT and some people are having a lot of success and some people aren't. It could be the prompting, but it could also be that they're using different models.

0:04:44 - (Alane): Well. And depending on their position within the company of how they're using it. So, you know, just like we were talking about data and spreadsheets might be better for a different model. One thing with Claude that I do love, and you don't need this all the time, is the deep research.

0:04:59 - (Micah): Yeah.

0:05:01 - (Alane): And being able to create a project where you're training or describing what you're trying to do, and then have it do deep research for you, and it just takes a couple of minutes for it to dig in, really start processing, and it kind of is walking you through what it's thinking while it's doing it, and then produces the end result. Then you can continue having a conversation with it after it produces the deep research.

0:05:27 - (Micah): Yes, yes. So I would even tie all of this back to automation. Alane, where when you're thinking about creating automation, and this is, this is part of a lot of what we consult our clients on, is depending on what you're automating, you may not need the V12 engine of AIs to do part of the automation. You may need the GPT 41 minis that are going to be super inexpensive, super fast and return exactly what you need for that part.

0:06:01 - (Micah): But then later in the automation, you might want a more heavy hitter. So you might want to get Claud Sonet in there and you can mix and match. You know, that's where the car analogy falls apart. You wouldn't be like driving a Chevy a block and then jumping into a Jeep usually. But when it comes to automation, that's exactly what can happen. You can start with a really small, cheap, inexpensive, quick GPT model and then instantly switch to Claude Sonnet 4 and run a little bit of a heavy hitter to produce complicated content or fill in templates or produce a slide deck or whatever it might be.

0:06:37 - (Micah): Picking and choosing and testing that out and optimizing is where all of this comes back into play.

0:06:45 - (Alane): And there's different things. Like sometimes I might just want to do like a quick feasibility and just need something quick. So I'll use one of the ChatGPT models just for quick feasibility to see, hey, is this going to give us a good result if we put this into an AI agent? If I want to dig a little bit deeper, you know, I might use Claude. And then Perplexity has a really good use case too with live links, live web research, things like that. Because the other models, I think that there's some misconceptions that I hear is that all of the AI models are using web data. It's basically a search engine, whereas that's maybe one of the data points it's using, but it has so many other data points that it has been trained on outside of just what's on the Internet.

0:07:33 - (Micah): Yep. And those training, those data points for the training for the specific models, that's why there's versions of models, because there are cutoff dates. So to a point it's knowledge ends and then it has to rely on web search from there. And then that's only as good as the search that it's doing what it finds at the time and brings it back in. So that's very different than the training data, than it has whereas Perplexity is, is closer to a search engine I would say because it can search live data and then bring back the sources and then it typically runs it through one of the other models to process that data, which is pretty interesting.

0:08:15 - (Alane): I've been using it. This is kind of a unique use case for reading Google reviews and pulling for a certain business, pulling Google reviews that mention something specific and say can you pull these places that have mention this in their Google reviews and in a certain area you can't say throughout the United States or throughout Canada. You know, it's too much like the AI couldn't pull all that data.

0:08:45 - (Alane): It blows out the context window. But that does work. And I just put it in a specific location and you have this keyword and say hey, can you pull these types of businesses that mention this in their Google reviews in this location? And it'll pull those on there. So that one's kind of a fun. Where it is pulling that kind of more live, accessible Internet data.

0:09:08 - (Micah): Yeah, that's super cool actually. So I would say even outside of automation, if you're, if you and your team are just chatting with AI and getting the benefits of that right now, definitely experiment and for the most part I wouldn't even say lock yourselves into one. You have multiple models with GPT. You can experiment with them all and figure out what's working best. But it is a case by case basis and there's times where like we'll even be chatting internally with Claude at one point and then go, this isn't really where we need it and we'll switch over to GPT and see if we can get a better result and we change the model, see if we can get a better result.

0:09:53 - (Micah): So it is a little bit of trial and error, but it is case by case. In one case you might want to parse some stuff out of an Excel file and get it into a structured JSON list or a bulleted list or whatever you want to do with that. Have it send a PDF. Some models are going to be strong enough to do that on the first try. But if you're not getting great results on the first model, don't keep trying with the same model.

0:10:16 - (Micah): Check a different model, check a different provider, see what works and you might find that that's going to be a nice time saving shortcut. Typically my daily driver, so to speak, I start with Claude and when I'm running into roadblocks there, I'll switch back over to GPT. But we have both at our fingertips to maximize the ability to use either one we need at any case.

0:10:42 - (Alane): Yeah, I think that's a really good point that it's not about picking one and sticking with it. I think you would be doing yourself a disservice because your day, unless your role is just very specific and you're doing the same thing daily most of the time we're doing a lot of different things and the right tool, you want to use the right tool for that specific use case. And so like you said, you've got access to whichever one and you're going to try it out.

0:11:08 - (Alane): I like, I usually go to GPT first and then switch over, but I think that's just habit because that's what came out, you know, mainstream first that we would use and then so changing my habit there. But I use Claude and Perplexity all the time as well. But I do like just going to GPT first for feasibility. Am I on the right track? Here's an idea that I have. I want to see what the output is and then I'll go, you know what, this is actually a better use case for Perplexity. Like I wouldn't have gone to per GPT for my Google Reviews idea.

0:11:43 - (Alane): That's just not the right use case for that. So making sure. And it is trial and error and it is. And it is breaking habits because we can get locked into just using one by going and creating your accounts on the other one, playing around, seeing what type of feedback that you get and really starting to understand which ones are the best use cases. When I want to do deep research, I'm going to Claude for sure that one does the best job for that.

0:12:10 - (Micah): Yeah. So the final thing that I would throw out is once you start experimenting and trying these different models, one, share it across your team because your other team members need to know this information as well. It's only going to make you stronger as a company. But two, if you find yourself doing the same thing over and over, where I need to go to Claude or I need to go to GPT and I need to give it this input and I'm looking for this output and then I'm copying and pasting that and then I'm doing something with that, that's when you need to operationalize it and go, hold on, why am I still doing all this manual work?

0:12:45 - (Micah): That's when you need to start automating it so you can start thinking about what is the trigger. Why would I start going to CLAUDE or GPT? What am I pasting in there? What am I trying to get out of there and where am I putting it? And if you can define those, you have the basics for an automation workflow.

0:13:06 - (Alane): And to wrap this up, we have a 25 AI prompts for you with different use cases depending on your department. We've got it broken up so that like the Google Reviews idea, there's so many good ideas and use cases that we've created and it'll be linked in the show Notes. Thanks for listening to this episode of Automate Your Agency. We hope you're inspired to take your business to the next level. Don't forget to subscribe on your favorite podcast platform and leave us a review.

0:13:35 - (Alane): Your feedback helps us improve and reach more listeners. If you're looking for more resources, visit our website at biggestgoal.ai for free content and tools for automating your business. Join us next week as we dive into more ways to automate and scale your business. Bye for now.