Perplexity vs ChatGPT: I Ran 10 Prompts to See Who Wins

Everyone’s comparing AI chatbots — but what happens when one of them is not a chatbot at all? 

That’s what immediately intrigued me about Perplexity AI. It brands itself as an 'AI-powered answer engine' — a citation-rich, intelligent alternative to Google. Yet, in practice, it often feels like a chatbot, delivering answers directly, albeit with a strong research backbone.

I’ve been using it since it first launched in late 2022, right around the time ChatGPT exploded onto the scene. Needless to say, I found myself constantly switching between the two, testing ideas, writing drafts, and digging into research. And while they seem to serve different purposes on paper, in reality, there’s a lot of overlap.

So I finally did it: Perplexity vs ChatGPT, head to head. Same prompts. Same tasks. Same expectations. From fact-checking to content creation, I wanted to see which one actually delivers more value when you’re deep in the flow of work.

And here’s what happened, all with G2 data to back it up

Note: Both OpenAI and Perplexity AI frequently roll out new updates to these AI chatbots. The details below reflect the most current capabilities as of April 2025 but may change over time.

Perplexity vs. ChatGPT: What’s different and what’s not?

After spending a lot of time with both tools, I started to notice a pattern. On the surface, they often feel similar — both respond conversationally, both can tackle a wide range of prompts, and both are powerful AI assistants in their own right.

But once I started using them for deeper research, writing help, and day-to-day tasks, the differences (and surprising similarities) became impossible to ignore.

Perplexity and ChatGPT: The differences

Think of Perplexity as a research librarian and ChatGPT as a creative writing coach: one delivers sources with precision, and the other crafts flow and structure. Here’s how they stack up:

• Positioning and purpose: From what I’ve seen, ChatGPT is clearly designed as a general-purpose AI chatbot — creative, conversational, and customizable. Perplexity, on the other hand, positions itself as an “answer engine” built for fast, accurate, and sourced responses. It feels more like a smart, AI-powered alternative to Google than a classic chatbot.
• Interface experience: ChatGPT feels like a chat app at its core. It’s highly capable and designed for longer, multi-turn conversations. With chat history, custom GPTs (tailored using instructions, files, or functions), and built-in tools like browsing and image generation, it creates a flexible space for creativity and problem-solving. Perplexity, on the other hand, leans into its identity as a search-first tool. Its interface resembles a research engine with a chat overlay optimized for fast, citation-rich responses. It also includes features like Discover for trending topics and Spaces to save answers, organize research, or build lightweight custom AI assistants.
• AI models and processing power: ChatGPT runs on OpenAI’s GPT models, while Perplexity stands out for offering a variety of LLMs from multiple providers, including Claude 3.5 Sonnet, GPT-4o, Gemini Flash, and more. It also features its own models like Sonar and R1 1776, giving users greater flexibility and control.
• Citation-first vs. chat-first: Perplexity always shows sources. Citations are built into every answer. With ChatGPT, you’ll only get sources when using the web browsing tool (and even then, they're less prominent.)
  
• Memory and continuity: ChatGPT supports memory across sessions, which means it can remember preferences, past prompts, or context I’ve shared (super useful for ongoing workflows.) Perplexity doesn’t have long-term memory, at least from what I’ve observed. So, each session often starts fresh.

Perplexity and ChatGPT: The similarities

Despite the branding and features, they still have a lot in common when it comes to getting stuff done.

• Text generation: Both tools are great at generating clear, human-like responses to questions, prompts, and creative tasks. Whether I’m summarizing an article, drafting a blog intro, or rephrasing something for tone, both ChatGPT and Perplexity deliver coherent, context-aware output.
• Coding: While ChatGPT has the edge for more advanced dev tasks, Perplexity still holds its own for quick code explanations, syntax help, and debugging suggestions. I’ve used both for everything from basic HTML and Python snippets to exploring new frameworks.
• Voice interactions: Both platforms now support voice chats. I’ve used ChatGPT’s voice mode for casual conversations or quick prompts on the go, and Perplexity recently rolled out voice capabilities, too. It’s great when I want fast answers without typing.
• Multimodal capabilities: Each platform supports multimodal input in different ways. ChatGPT lets me upload images and have the model describe, analyze, or interpret them. Perplexity isn’t natively built for image generation, but it can extract insights from web visual content and generate images with the paid plan.
• File analysis: Both tools let you upload files, like PDFs, docs, or decks, for summarization and Q&A. I’ve used them to extract key insights from dense research papers in seconds. Perplexity supports plain text, code, PDFs, and images up to 25MB, with 3 uploads per day for free users and up to 4 files at once. ChatGPT supports formats like DOCX, PDF, TXT, PPTX, CSV, and XLSX (up to 512MB each), with free users again limited to three uploads daily.
• Web search and deep research: Both ChatGPT (via SearchGPT) and Perplexity provide real-time web access, but their styles differ—ChatGPT summarizes, while Perplexity highlights sources and citations by default. Both also offer a Deep Research feature that pulls from multiple web sources to generate structured, in-depth reports. I’ve found it great for tackling complex topics or multi-layered questions.
• Temporary chats and sharing: Both tools support temporary sessions and make sharing easy. I often share Perplexity threads by downloading them as PDFs or text files, while ChatGPT offers shareable chat links.
• Custom AI assistants:  While ChatGPT has “custom GPTs” and Perplexity has “Spaces,” both let me build purpose-specific AI tools. For example, I could create a writing assistant that remembers my preferred style in a custom GPT, or a Perplexity Space focused on tracking stock market news with specific sources. Both platforms make it easy to customize how the AI responds and remembers context (within that session).

Comparing specs is one thing. But how do Perplexity and ChatGPT hold up in practice? Here’s how I put them to the test.

Disclaimer: AI responses may vary based on phrasing, session history, and system updates for the same prompts. These results reflect the models' capabilities at the time of testing. Also, this review is an individual opinion and doesn’t reflect G2’s position about the mentioned software’s likes and dislikes.

Perplexity vs. ChatGPT: How they actually performed in my tests

Now, the crucial question: How did Perplexity and ChatGPT fare? For each test, my analysis will follow this structure:

• Key observations: A look at each tool’s strengths, weaknesses, and any standout surprises — good or bad.
• The superior choice: My take on which model did better based on accuracy, creativity, clarity, and how usable the output was.
• Final judgment: My direct call on which tool emerged victorious for that specific task.

1. Summarization

The first challenge involved summarizing. I instructed both ChatGPT and Perplexity to extract the key information from a G2 article detailing the growing adoption of Canva by non-designers, presenting it in exactly three bullet points and under 50 words.

Perplexity's response to the summarization prompt

Right away, I noticed a difference in how they approached the task. Perplexity kept things clean and direct. Its bullets were short, skimmable, and stayed true to the brief.

ChatGPT's response to the summarization prompt

ChatGPT, meanwhile, added more detail and depth, even bringing in G2 review data, which was impressive but went a bit overboard for a tight summary. It felt more like a mini editorial than a TL; DR.

While both were accurate, Perplexity’s version was easier to use at a glance. So if I needed something polished for a write-up, I might lean on ChatGPT. But for quick, high-impact summaries, especially under word limits, Perplexity did a better job.

Winner: Perplexity

2. Content creation

Moving on to AI content creation, a known strength, I wanted to see how Perplexity and ChatGPT would perform under the pressure of a full marketing push.

So, I gave them both a pretty comprehensive single prompt, asking for product descriptions, catchy taglines, social media posts for different platforms, email subject lines to draw people in, and even a short script for a video ad. Basically, the whole nine yards of a marketing campaign!

Both ChatGPT and Perplexity handled it really well. The outputs were polished, varied, and genuinely usable. Interestingly, the ideas they came up with were pretty similar across both tools, which made the comparison feel even fairer.

Perplexity’s output was strong. Its tone shifted nicely between platforms — playful on Instagram, straightforward on email, and visual on video. 

Perplexity's response to content creation prompt

I found its tagline punchier than ChatGPT’s. Its copy didn’t feel templated, and I liked that it didn’t need much tweaking. I especially appreciated how naturally it handled different formats without losing brand voice.

Perplexity's response to content creation prompt

ChatGPT made the content feel ready to drop into a brand doc or campaign deck. It also offered more hashtag options for social media posts, which is helpful if you're trying to cover multiple angles or tap into different trends. The tone across formats was consistent, and I didn’t spot any weaknesses in its approach.

ChatGPT's response to content creation prompt

Bottom line: both tools performed impressively here. I didn’t feel like either one lagged. If I had to choose, I’d say it’s a tie; ChatGPT wins on structure and extras (like hashtag coverage), while Perplexity stands out for its fluid tone and plug-and-play readiness.

Winner: Split verdict.

3. Creative writing

I really wanted to see how well these tools could break away from formulaic outputs and actually tell a story with mood, pacing, and a twist. I gave both ChatGPT and Perplexity a sci-fi prompt with a few must-have elements: a mysterious signal, a sentient AI, and a reality-bending reveal — all within 300 words.

Right off the bat, ChatGPT stood out for including a title, “Whispers of the Wanderer,” which instantly set the tone. Its story had atmosphere, tension, and a cinematic feel. The pacing was tight, the language vivid (especially those descriptions of the nebula and the glitching hologram), and the ending twist, “You’re the signal, ”landed perfectly.

ChatGPT's story "Whispers of Wanderer" for the creative writing task

Perplexity’s take was also strong. It built a different kind of mood, which was more philosophical and almost dreamlike. The narrative had a softer tone but still hit the key elements. The final line, "Reality is not what you see, but what you are allowed to see," was a powerful closer. It leaned slightly more abstract, but I liked that it took a different stylistic route than ChatGPT’s version.

Perplexity's response to my creative writing prompt

Both stories had depth and solid character voice and used the elements I asked for. So overall? Another strong showing from both. 

Winner: Split

4. Coding  

Full disclosure: I’m not a developer. But I do know that coding is a major benchmark for AI performance, especially when it comes to real-world use. For this test, I asked both ChatGPT and Perplexity to build a simple password generator using HTML and JavaScript. I wanted to get a working solution with clean code and a user-friendly interface.

And this round? ChatGPT swept it. The code it generated worked perfectly on the first try. The interface was clean and intuitive, and the tool did exactly what it promised — no hiccups. Even as a non-dev, I could understand what the code was doing, and the overall setup looked polished enough to drop into a beginner project or a quick demo. I liked that it had styled the UI also better with the lock emoji and colorful buttons.

ChatGPT's password generator

Perplexity, on the other hand, produced a mostly functional version, but the clipboard copy didn’t work. That might seem like a small detail, but it made the whole experience feel less complete. The UI also wasn’t quite as refined. It did the job, but lacked the little touches that made ChatGPT’s version feel more usable and polished.

Perplexity's password generator

Winner: ChatGPT

5. Image generation

Next, I wanted to test something a little more visual — image generation. We’ve all seen AI-generated art floating around online, but I was curious to see how well these tools could handle something grounded and realistic: a stock photo of a small business owner. It’s the kind of image marketers, content creators, and small teams constantly need. But generating one that actually looks believable? That’s a real challenge.

It’s worth noting that Perplexity lets only Pro users generate images as part of their workflow, and I tried it with both Flux and DALL·E 3 on the platform. 

ChatGPT, using GPT-4o, gave me what felt like the best overall interpretation. The setting looked like a cozy boutique, complete with a mix of products — clothes, accessories, and a warm, modern vibe. It checked most of the boxes in a balanced, visually clean way.  

And given all the recent buzz around GPT-4o’s image generation capabilities, being able to create everything from Ghibli-style art to ultra-realistic photos, it truly delivered here. The image looked polished, natural, and very usable for real-world content.

Image generated with ChatGPT

Perplexity’s image generation experience was a bit less intuitive. I had to dig through the interface to actually find the option since it wasn’t front and center like in ChatGPT. But once I got there, the results were solid. The version generated through Flux leaned into the atmosphere more than detail. It didn’t cover every aspect of the prompt, but it got the cozy, inviting mood right. If ambiance was the goal, this was a win.

The DALL·E 3 version from Perplexity was the most product-specific. It zoomed in on the handmade and textile angle, giving off serious artisanal shop vibes. Think carefully stacked fabrics, sewing tools, and a workspace that feels curated and authentic. If I were promoting a craft-focused small business, this would be the one I’d reach for.

Image generated with Perplexity

So, which tool was better? Honestly, it depends on what you’re prioritizing. ChatGPT was the best all-rounder, Flux delivered on vibe, and DALL·E 3 on product specificity. Different flavors, all impressive — but ChatGPT definitely felt the most seamless to use.

Winner: ChatGPT

6. Image analysis 

For image analysis, I really wanted to push Perplexity and ChatGPT a bit further. Instead of a simple picture, I gave them two distinct types of visuals: an infographic about AI adoption and a handwritten poem. And honestly? Both tools did surprisingly well.

Perplexity offered clear summaries for both images, highlighting key trends in the infographic and interpreting the poem’s visual elements with ease. It pulled out the most important percentages, offered insights about design choices, and interpreted the poem with emotional nuance. No major red flags there!

Perplexity's response to my image analysis prompt

ChatGPT also did a solid. But it was way more structured than Perplexity’s with subheaders. For the handwritten poem, it went the extra mile and fully transcribed the poem, which I found super helpful. That little bit of added structure made it easier to skim.

ChatGPT's response to my image analysis prompt

First, the infographic. ChatGPT gave me a super well-structured summary, hitting all the key statistics, trends, and conclusions. It even gave me some thoughts on the visual design, which was a nice touch.

If I had to nitpick, the poem transcription by ChatGPT was the one standout difference — otherwise, they were fairly evenly matched. I didn’t find either missing any critical observations or misinterpreting anything. Both tools demonstrated strong comprehension and interpretation skills.

ChatGPT transcribing my hand-written notes as part of the image analysis task

So, in this round? It’s a close call. If you're just looking for fast, accurate image understanding, Perplexity absolutely holds its own. But if you value slightly more structure and that bonus level of detail like transcription, ChatGPT nudges ahead.

Winner: ChatGPT

7. File analysis

For this task, I gave both ChatGPT and Perplexity a heavy-hitter: Einstein’s 1905 paper “On the Electrodynamics of Moving Bodies” and asked them to summarize it in five bullet points under 100 words.

ChatGPT’s response was polished, accessible, and grouped ideas like time dilation and length contraction well. It felt user-friendly and clear without oversimplifying. It went a little above word count but still not as much as Perplexity. 

ChatGPT's response to the file analysis task

Perplexity’s take leaned more academic, using terms like “Lorentz transformations” and “mass-energy relationship” right up front. It felt like something a researcher might write — precise, slightly more technical. It definitely went a little over the word count, too. 

Perplexity's response to the file analysis task

Winner? Discounting the word count issue, I’d say it’s a tie. ChatGPT is great for quick understanding. Perplexity feels more formal. Both are excellent at distilling complex content into digestible insights.

Winner: Split verdict. 

8. Data analysis 

Data analysis was next. I provided both with a CSV of U.S. ChatGPT search interest by subregion to see who could extract key insights.

And I have to say, Perplexity knocked it out of the park. It didn’t just summarize the data; it broke it down with the kind of detail I didn’t get from ChatGPT, Gemini, or even DeepSeek. We're talking about statistical summary with mean, median, and standard deviation, along with thoughtful observations under regional patterns.

The technocentric insight you see below? Super valuable. It made me feel like I was reading the analysis of someone who really got the data, not just glanced at it.

Perplexity's response to the data analysis prompt

ChatGPT did fine — clean, readable, and accurate. If I wanted a quick scan, sure, it delivered. But if I was prepping for a meeting or writing a report, I’d lean on Perplexity for the extra depth.

ChatGPTs response to the data analysis prompt

This one wasn’t even close. Clear win for Perplexity.

Winner: Gemini 

9. Real-time web search

In the next task, I was curious to see how well Perplexity and ChatGPT could keep up with the world. I asked them to find and summarize the three most recent AI news stories.

ChatGPT surprisingly rose to the occassion. It's real time web search pulled timely headlines — White House appointing Chief AI Officers, Meta launching Llama 4, and Microsoft showcasing an upgraded Copilot at its 50th anniversary event. Not only were the stories fresh (all dated within the past few days), but the summaries were concise, structured, and full of context.

ChatGPT's response to the real-time web search task

Perplexity, on the other hand, took a more analytical approach. It pulled in deeper industry-wide stories like the AI Index Report, Google's Pixel 9 Gemini AI update, and how AI is reshaping MBA programs. These were definitely insightful and sourced from credible publications like Nature, but they weren’t as “news-now” as what I got from ChatGPT. Some headlines felt more like recent research findings or product commentary than hot-off-the-press updates.

ChatGPT's response to the real-time web search task

For me, ChatGPT came out on top for this one. 

Winner: ChatGPT

10. Deep research task

The final task I designed was centered around what I believe is a truly pivotal capability for AI chatbots: deep research. The promise of these tools to tackle complex research questions and efficiently analyze vast amounts of information is incredibly exciting.

To test this directly, I set both Perplexity and ChatGPT the challenge of exploring a current and significant area: the ongoing trends in SaaS consolidation.

Perplexity responded quickly and packed its analysis with up-to-date data — 49 sources in total. It nailed the numbers, cited recent case studies, and delivered a clean summary with strong financial context. The insights around tech hubs and valuation trends were especially sharp. That said, it was more of a straight data drop — fast and accurate.

Perplexity’s deep research capabilities

ChatGPT, in contrast, took its time. It asked me clarifying questions first, which made the experience feel more collaborative. The final report took about eight minutes, but it was worth the wait. It pulled from 41 sources, included examples, and had a clear strategic structure.

I did notice it leaned on older data, which was a bit frustrating since I’d asked for insights from the last 3–5 years. Still, the content was rich and layered, with thoughtful takeaways for SaaS leaders and investors.

ChatGPT's Deep Research asking questions before starting

ChatGPT, on the other hand, took a more interactive approach, asking me detailed questions about my preferred timeframe, geographic focus, and priority areas before proceeding. Compared to Gemini's faster turnaround, ChatGPT took longer to complete the task (about eight minutes to generate the entire report).

Both tools did really well, but in different ways. Perplexity is better for quick, data-heavy research. ChatGPT takes longer but gives you something closer to an executive briefing.

You can find both research reports here.

Winner: Split verdict

Perplexity vs. ChatGPT: Head-to-head comparison table

Here’s a table showing which chatbot won the tasks.

Key insights on Perplexity and ChatGPT from G2 data

I also dug into G2 review data to uncover how users rate and adopt ChatGPT and Perplexity. Here’s what popped: 

Satisfaction ratings

• ChatGPT leads in overall satisfaction, with especially high marks for ease of use (97%), setup (96%), and doing business (94%). It consistently outperforms the category average in nearly every metric.
• Perplexity also scores well, matching or beating ChatGPT in ease of setup (97%) and ease of use (95%), but has fewer reviews.

Top industries represented

• ChatGPT sees broad adoption across tech-forward sectors, with most reviews coming from IT services, software, and marketing. It’s also gaining traction in financial services and higher education.
• Perplexity’s footprint is narrower but similar — dominated by software, IT services, and media — suggesting early adoption among researchers, developers, and content teams.

Highest-rated features

• For ChatGPT, the top-performing features are understanding (92%), natural conversation (91%), and user interaction learning (91%), making it feel intuitive and easy to engage with.
• Perplexity stands out for complex query handling (88%), which is no surprise given its research strengths, and it also scores well for natural conversation and learning from user interactions (both at 87%).

Lowest-rated features

• ChatGPT’s lowest scores are in data security, content accuracy, and error learning, though these still hover around the category average.
• Perplexity’s weaker points are more noticeable: API flexibility (74%), software integration (77%), and error learning (80%) fall well below average, indicating friction for teams looking to build it into workflows with the tool. 

Perplexity vs ChatGPT: My final verdict

After putting both tools through a full range of real-world tests, here’s my takeaway: ChatGPT is still the most consistent all-rounder. It performs well across nearly every task—content creation, creative writing, coding, and real-time updates — with a solid mix of accuracy, structure, and ease of use.

But Perplexity genuinely surprised me. It’s the only AI I’ve tested — compared to Gemini and DeepSeek — that came this close to ChatGPT across so many tasks. In fact, it scored multiple split verdicts and even beat ChatGPT outright in some areas.

If you're after depth, speed, and citation-heavy outputs, Perplexity is a strong pick. But if you need a well-rounded assistant that balances creativity, structure, and flexibility, ChatGPT still leads the pack.

Bottom line? You can’t go wrong with either — but which one’s better depends on what you’re trying to get done.

Frequently asked questions on Perplexity and ChatGPT

Still have questions? Get your answers here!

1. How does Perplexity AI work?

Perplexity is an AI-powered answer engine that combines natural language processing with real-time web search. It generates responses by pulling data from live sources and large language models (LLMs) like GPT-4, Claude, and its own Sonar models. Every response includes citations, making it ideal for research and fact-based queries.

2. Perplexity vs. ChatGPT: Which is better?

It depends on what you’re using it for. ChatGPT is more versatile overall — great for content creation, coding, and creative tasks. Perplexity excels at fast, citation-backed answers, deep research, and summarization.

3. Perplexity Pro vs. ChatGPT Plus: What’s the difference?

Both are premium plans priced at $20/month, but they offer slightly different experiences.

• ChatGPT Plus gives you access to GPT-4o, image generation, file uploads, memory, and custom GPTs.
• Perplexity Pro unlocks faster response times, image generation (via Flux and DALL·E 3), higher file upload limits, and model-switching between advanced models from different providers like GPT-4, Claude 3.7 Sonnet, Gemini 2.5 Pro and others.

4. Is Perplexity free to use?

Yes, Perplexity has a free version that has unlimited free searches, three Pro searches per day, and live web results. Perplexity Pro (paid) unlocks access to multiple advanced models, image generation, faster response speeds, and larger file support.

5. What models do Perplexity and ChatGPT use?

ChatGPT uses OpenAI’s GPT models, including GPT-4o, GPT-4, o1, etc. Perplexity supports a variety of models, like Claude 3.7, GPT-4, Gemini 2.5 Pro and its own Sonar models — letting you switch between them as needed.

6. Can ChatGPT and Perplexity access real-time information from the web?

Yes. ChatGPT can access real-time info using SearchGPT, its browsing tool. Perplexity has real-time web access built in (even in its free version) and includes clickable sources in every response.

7. ChatGPT vs. Perplexity: Which one is better for research?

Perplexity is great for quick, source-heavy research. It even allows you to select the source of search like web, social media sites and forums like Reddit or X and academic papers. ChatGPT is better if you want a more structured, strategic report or need follow-up clarification.

8. Are the responses accurate on ChatGPT and Perplexity?

Both tools are generally accurate, but like any AI, they’re not perfect. Perplexity is rated slightly higher on G2 for content accuracy. ChatGPT offers more structured explanations but can occasionally reference outdated data if browsing is off.

9. Can I use both ChatGPT and Perplexity?

Absolutely! Many users combine both — ChatGPT for brainstorming, writing, and structured coding while using Perplexity for research tasks.

ChatGPT and Perplexity aren’t the only AI chatbots out there. I’ve tested Claude, Microsoft Copilot,  and more to see how they stack up in my best ChatGPT alternatives guide. Check it out!