Hey everyone, hope you're doing well. So, there's a lot of buzz flying around about GPT-5, OpenAI's latest and greatest model that officially dropped around August 7th, 2025. If you've been playing around with AI as much as I have, you've probably noticed something interesting. You might run the same query on ChatGPT with GPT-5 & then try it on a platform like Perplexity, which also uses OpenAI's models, & get a response that feels... well, smarter.
It's a weird feeling, right? It's the same engine under the hood, so what gives? Is Perplexity secretly using a supercharged, souped-up version of GPT-5? Or is there something else going on?
Honestly, it's a question I've been digging into a lot lately, & it turns out the answer is pretty fascinating. The short version is: the platform matters. A LOT. It’s not just about the raw intelligence of the Large Language Model (LLM) itself. The ecosystem it operates in—the data it can access, the features it's wrapped in, & the very purpose it's designed for—can totally change how "smart" it feels to us as users.
Let's break it down.
First Off, What's the Big Deal with GPT-5 Anyway?
Before we get into the platform debate, let's just take a second to appreciate the beast that is GPT-5. This isn't just a minor update. OpenAI really went for it, unifyingめちゃくちゃ advanced reasoning, multimodal capabilities (so, text, images, voice, all that good stuff), & task execution into one seamless system. The goal was to create a model that's not just a better chatbot, but something that can handle complex, multi-step workflows.
According to the folks at OpenAI & the initial reviews, GPT-5 is a significant leap forward in a few key areas:
- Reduced Hallucinations: One of the biggest gripes with previous models was that they'd sometimes just... make stuff up. Confidently. GPT-5 is supposedly way less likely to do this, with some tests showing a 45% reduction in factual errors compared to GPT-4o when web search is on.
- Deeper Reasoning: It’s designed to handle more complex, multifaceted questions without needing you to hold its hand & spoon-feed it prompts. Think questions like, "How will rising interest rates impact homeownership trends for Gen Z in mixed urban & suburban markets?"—the kind of stuff that would've taken multiple tries before.
- Better at Following Instructions: It’s just generally more reliable & less likely to give you those frustratingly generic, canned responses.
- Different "Sizes": OpenAI released a whole family of GPT-5 models, from a massive "Pro" version for high-end subscribers to "mini" & "nano" versions that are more efficient. This allows platforms to choose the right tool for the job.
So, we've established that GPT-5 is a seriously powerful engine. But an engine is only as good as the car it's in. And that's where the platform conversation really begins.
The "Answer Engine" vs. The "Conversational Partner"
Here’s the thing: Perplexity & ChatGPT are built for fundamentally different purposes, & that design philosophy changes EVERYTHING.
Perplexity: The AI Research Assistant
Perplexity isn't trying to be your creative brainstorming buddy or your coding co-pilot. It positions itself as an "answer engine." Think of it as a supercharged search engine. Its entire architecture is built around one core mission: to find the most accurate, up-to-date, & verifiable information on the web & present it to you with sources.
This "research-first" approach is why GPT-5 can feel so much more intelligent on Perplexity when you're asking for factual information. Here's why:
- Real-Time Web Access is the Default: Unlike ChatGPT, where you might need to specifically enable a browsing mode, Perplexity is ALWAYS connected to the internet. It doesn't rely solely on the static data it was trained on, which has a cutoff date. If you ask about something that happened yesterday, it will find it. This is HUGE for topics that are constantly evolving.
- Citations are King: This is probably Perplexity's killer feature. It doesn't just give you an answer; it shows you its work. You get footnotes & links to the exact sources it pulled information from. This transparency not only builds trust but also allows you to dive deeper & verify the information for yourself. It feels smarter because it's behaving like a diligent, well-sourced researcher.
- Focus on Conciseness & Accuracy: Perplexity is designed to synthesize information from multiple sources & give you a concise summary. It's less about a long, flowing conversation & more about getting you the answer you need, fast.
So, when you ask Perplexity a question, you're not just interacting with GPT-5. You're interacting with GPT-5 supercharged by a powerful, real-time search & summarization layer. It’s like giving the world's smartest person a direct, high-speed line to the entire Library of Congress & every news feed on the planet. Of course they're going to seem smarter.
ChatGPT: The All-Purpose Creative Powerhouse
ChatGPT, on the other hand, is designed to be a much more versatile, general-purpose tool. It's the original conversational AI that took the world by storm. It's meant to be a writer, a coder, a brainstorming partner, a data analyst, & a friendly chatbot all rolled into one.
Its strengths lie in a different kind of "smart":
- Creative Generation: This is where ChatGPT shines. Ask it to write a poem, draft an email in a specific tone, come up with marketing copy, or create a fictional story. Its fine-tuning for conversation & creativity makes it incredibly powerful for these tasks.
- Contextual Conversation: ChatGPT is built to remember the flow of your conversation & build upon it. You can have long, winding chats, refining your ideas as you go. It's less of a question-and-answer machine & more of a collaborative partner.
- Versatility & Integration: With the GPT Store & custom GPTs, you can essentially create specialized versions of ChatGPT for almost any task, from creating flowcharts to analyzing data from an uploaded file.
When you're using ChatGPT, you're tapping into the raw, creative, & conversational power of GPT-5. It feels "smarter" when you need a collaborator, not just a fact-checker. The problem is, when you ask it for hard facts, it might rely on its training data, which can be outdated, or its web search might not be as deeply integrated as Perplexity's. It can sometimes "confidently hallucinate" because its primary directive is to provide a plausible, human-like response, not necessarily a sourced, factual one.
It’s Not Just the Model, It’s the Scaffolding
This whole phenomenon highlights a critical concept in AI today: the importance of the "scaffolding" around the model. The LLM is the core intelligence, but the platform provides the infrastructure that directs & enhances that intelligence.
Think about it like this: a brilliant actor can deliver a good performance on a bare stage. But put them on a detailed set, with the right lighting, costumes, & props, & their performance becomes infinitely more believable & impactful.
The platform is the set, the lighting, & the props. It provides:
- Data Access: Does the model have access to real-time information? Can it connect to proprietary databases?
- User Interface (UI): How does the user interact with the model? Is it a simple chat window, or a more structured interface with options & filters?
- Feature Set: What other tools are wrapped around the model? Things like citation generation, data analysis tools, or integration with other apps.
This is where things get REALLY interesting for businesses. The idea that you can take a powerful, general-purpose model like GPT-5 & wrap it in a custom platform to make it "smarter" for a specific need is a total game-changer.
Bringing That "Smarter" AI to Your Own Turf
This is exactly why we're seeing a massive boom in custom AI solutions for businesses. Companies are realizing they don't just need a generic chatbot; they need an AI expert trained on their world.
Let's say you run an e-commerce site that sells high-end coffee equipment. A customer lands on your page & has a super specific question: "What's the optimal grind size for a light roast Ethiopian Yirgacheffe on the Breville Barista Express, & how does that change if I'm pulling a single vs. a double shot?"
If you have a generic ChatGPT-powered bot, it might give a decent, general answer based on broad coffee knowledge from its training data. It might be helpful, but it won't be an expert on your products or the specific resources on your site.
But what if you could have an AI that's been exclusively trained on your product manuals, your blog posts about brewing techniques, your customer support logs, & your video tutorials? Now, you've got something special.
This is where platforms like Arsturn come into the picture. The whole idea is to let businesses build their own custom "scaffolding" around powerful AI models. With a no-code platform like Arsturn, you can create a custom AI chatbot that is trained on your own data. It helps businesses provide instant, 24/7 customer support by answering specific questions about their products & services. Instead of giving a generic answer, it can point the customer to the exact blog post you wrote about this topic, reference the product manual, & maybe even upsell them on your new line of single-origin beans.
This bot would feel infinitely smarter to your customer than a generic one, because it has specialized knowledge. It's not just smart; it's contextually smart. It's the difference between talking to a random coffee enthusiast & talking to your company's most experienced barista.
This approach is becoming critical for everything from lead generation to customer engagement. When a potential lead lands on your website, you don't want them to get lost. A tool like Arsturn allows you to build a no-code AI chatbot trained on your business data that can engage them immediately, answer their questions with precision, & guide them toward becoming a customer. It helps boost conversions by providing a personalized, expert experience that a generic AI just can't match.
The next time you feel like one AI seems "smarter" than another, even if they're using the same base model, remember that you're not just seeing the AI. You're seeing the AI in its habitat.
Perplexity feels smarter for research because its habitat is the live, searchable web, & its tools are designed for verification & citation. ChatGPT feels smarter for creativity because its habitat is an open-ended, conversational canvas.
And for businesses, the future isn't about just plugging in a generic AI. It's about building a custom habitat—a platform that gives the AI the specific knowledge & tools it needs to be an expert in your world. That's how you build a truly intelligent experience that feels like magic to your customers.
It's a pretty cool time to be watching all this unfold. The models are getting exponentially more powerful, but the real innovation is happening in how we harness that power.
Hope this was helpful! Let me know what you think. Have you noticed this difference between platforms? I'd love to hear your experiences.