Perplexity vs. Gemini: My Unfiltered Take After Real-World Tests

Whether browsing for a new recipe or putting together a presentation deck for work, we utilize AI chatbots like Perplexity or Gemini en masse to complete our personal and professional tasks.

That’s the current state of AI chatbots that can talk, converse, and assist others like real human beings. Now, AI chatbots can simulate emotions and sentiments, analyze academic papers and complex articles, and also become full-blown research assistants or data visualizers. 

After spending a lot of time working with AI chatbots, I decided to compare Perplexity and Gemini in a series of tests based on real-world tasks and problems. As the two most widely used AI chatbots, this experiment made it clear which ideal tasks are more suitable for either of these tools.

Right off the bat, Gemini proved to be better at slow thinking, creative narration, deep research, and sentient responses, while Perplexity offered ease of web browsing, accurate source citations, and structured thematic content. 

In addition to my comparison, I factored in hundreds of real-time G2 reviews that have rated Perplexity and Gemini quantitatively for each of these features. 

Whether it is conversational ability, writing, debugging code, writing poetic narratives, or generating quick social media emails, this list might help you figure out which one sails your boat.

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

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

While I set about testing two robust question-and-answer engines, I noticed one stark difference. 

While Gemini integrates with the larger Google ecosystem and is available on apps like Google Docs and Google Spreadsheets, Perplexity is more of a web browsing engine that offers automated contextual follow-up questions to make your search more immersive.

This interested me enough to research deeper nuances between the two — whether they converge and where they pull apart.

Perplexity vs. Gemini: Key differences

Based on my experience, these are the main differentiators between Perplexity and Gemini to keep in mind before working with them:

• Information retrieval and verifiability: Perplexity AI offers users superior transparency and trust through its technical design of real-time web indexing coupled with direct source citations, which is crucial for deep research tasks. It supports candidates in putting together academic papers, referencing authoritative case studies, and offering accurate sources of information for complete data analysis. Gemini, while using Google’s search infrastructure, provides less granular source attribution, which might be a concern for users who prioritize audit trails and source reliability.
• Native multimodal processing: Gemini’s technical architecture natively supports simultaneous processing and content generation across diverse data modalities (text, image, audio, and video). It offers users a unified platform for content creation, analysis, and interaction across various media types. Perplexity AI’s primarily text-based architecture, with nascent AI image generation in paid tiers, is a little limiting for users who want multimodal workflows. 
• Enterprise readiness and ecosystem integration: Gemini is deeply embedded in Google Workspace and Google Cloud ecosystems, allowing enterprise users to access AI assistance directly within Gmail, Docs, Sheets, and Drive, and to implement a secure model through Vertex AI and enterprise-grade governance. Perplexity offers an enterprise plan with collaboration features like shared threads and spaces, but lacks tight integration with productivity tools and cloud native infrastructure, limiting its plug-and-play appeal in enterprise environments.
• Knowledge graph and contextual awareness: Gemini benefits from Google’s vast knowledge graph, potentially offering richer contextual understanding and more nuanced responses within its integrated environment. Perplexity AI’s strength lies in its real-time web indexing, providing up-to-date information and a holistic search experience. But it potentially lacks the same depth of pre-existing structured knowledge for certain queries.
  
• Output controls and customization: You need to have proper control over output and tailor it to the prompt of the user to generate a satisfactory response. Perplexity allows users to toggle between multiple models (GPT-4, Claude, Mistral) for their responses, offering flexibility based on task requirements and preferences for tone, style, or performance. Gemini doesn’t yet expose model switching at the user level but instead focuses on fine-tuning outputs via prompt engineering, integrations, multimodal features, or emerging agentic features like “Gemini Live” or “Deep Think”.
  
• Visual and audio support: Gemini supports advanced image generation (via Imagen 2/4), image and video understanding, and native audio input/output, positioning it as a creativity tool as much as a productivity assistant. Perplexity, while offering image generation via third-party integrations like DALL-E or Playground AI, lacks some deep multimodal processing and is primarily optimized for textual content and search-based interaction.
• Web search and accessibility: While you can access Gemini on the web via gemini.google.com and use it, Perplexity offers you an option to set the default search engine on both desktop and Android. You can also access the Gemini plugin for Chrome, which helps summarize your search results. But Perplexity’s structured search engine and follow-up mechanism offer a far better web browsing experience.
• Image analysis: With Perplexity AI, you can use image uploads primarily to detect in-line text, thereby using object detection and contextual awareness. But it’s image generation is limited.  In contrast, Gemini boasts strong multimodal capabilities for comprehensive image analysis, including advanced recognition, OCR, and unlimited generation, deeply integrated within the Google ecosystem. While Perplexity emphasizes sourced information for image text, Gemini excels in broader visual understanding and creation across various applications.

Perplexity vs. Gemini: Key similarities

While Perplexity and Gemini offer slightly distinct research mechanisms, content style, and flow of speech, there are a variety of use cases that both of them can be collectively used for. Based on a common transformer architecture, both of these AI chatbots also have more things in common than you think.

• Conversational AI and chatbots: Both Gemini and Perplexity are widely used to build conversational agents that can understand and respond to user queries naturally. They power empathetic conversations and sustain a long-held context of user emotion as they understand text style, tone, and patterns to provide a satisfactory response to the user.
• Knowledge retrieval and question answering: Gemini and Perplexity both utilize retrieval-augmented generation to provide accurate answers. While Perplexity pulls real-time data from the web and cites live sources, Gemini integrates Google Search (in supported versions) to retrieve contextually relevant and trusted information.
• Content generation and summarization: Both Gemini and Perplexity excel at generating and summarizing content. Gemini shows strength in multimodal and technical content creation, and Perplexity delivers fresh, well-structured outputs by combining model strength (GPT-4 turbo) with real-time data grounding.
• Data analysis and insights extraction: Gemini and Perplexity can interpret structured inputs like tables or plain text to extract data-driven trends and summaries. Gemini integrates well with Google Sheets, while Perplexity interprets uploaded documents or links to generate concise data-driven insights and visualizations.
• Personalized recommendations and assistance: Both these tools offer an adaptive response based on user input and context. Gemini can personalize suggestions more deeply within Google Workspace, whereas Perplexity adjusts the tone and depth based on usage patterns and query style, even remembering the context from past threads when logged in.
  
• Multilingual support and translation: Both platforms support dozens of languages with high fluency. Gemini handles multilingual tasks with strong cross-language reasoning and honest feedback. Perplexity uses underlying models like Claude and GPT-4 to deliver accurate translations and understand the semantic context of code-switched queries. 

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. (Feel free to change based on the tools you are comparing).

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

Along with comparing both tools, it was also crucial to give a fair assessment of the benchmarks that they set in a specific task. As I evaluate these tools, I would structure my verdict in the following way.

• What stood out? I’ll highlight the strengths, weaknesses, or any surprises (good or bad) I noticed from both AI chatbots.

• Who did it better? I’ll inform you about which AI chatbot came out on top based on accuracy, efficiency, creativity, and how easy it was to use the output.
• Final verdict: I’ll share my honest take on which chatbot is a better choice for a particular task.

Ready? Here we go!

1. Summarization

For my summarization test, I asked both Perplexity and Gemini to summarize a G2 listicle (about the top construction estimating software for 2025) into a crisp TL;DR — within 100 words — highlighting the key shortlisting criteria.

The article discussed a first-hand analysis of the seven best construction estimating software for 2025 for buyers to refine their decision-making processes.

Prompt: Could you summarize the context in this G2 listicle in the form of a TLDR callout, which contains the major shortlisting parameters of software in the construction estimating software category, keeping your response under 100 words.

                         Perplexity’s response to the summarization prompt 

Perplexity’s response to the prompt really perplexed me (in a good way). While stating the obvious (shortlisting parameters), it surfaced the citations to both the original URL and the actual software category URL.

It also added the missed context around proprietary G2 scores and G2 user reviews that made the summary feel complete and grounded in authenticity.

Gemini, on the other hand, provided a neat and layered output, explaining what non-negotiable parameters are to keep in mind when you begin your research process for the best construction estimating software. It laid out metrics like user satisfaction, market presence, ease of administration, and implementation, which have been considered while ranking the products in the G2 listicle and are key influencing factors to invest in a worthy product. 

While the TLDR looks pretty decent and combines all the key parameters, it missed a major angle in the original listicle that provided more depth in the G2 listicle analysis: G2 reviews.

Winner: Perplexity

2. Content creation

Both Perplexity and Gemini have earned a reputation for producing high-quality, engaging, and audience-centric content that performs well across content distribution channels and improves lead generation.

For this task, I thought of putting both these tools to the test for a startup idea and instructed them to brainstorm content strategies, social media captions, scripts, ad copies, and so on. The goal was to create content marketing resources for a new product campaign.

I asked both products to generate marketing materials for a fictional product, “Mindgear”, which is a smartwatch that monitors your pulse, heart rate, sp02 levels, and blood pressure. It also comes with a built-in AI to detect your mood and align it with therapeutic voice instructions to calm you down. Marketing materials should ideally include product descriptions, taglines, social media posts, email subject lines, and scripts- essentially everything a brand would need for a full-on marketing campaign.

Prompt: Generate marketing materials for a fictional product “Mindgear”, which is an smartwatch that monitors your pulse, heart rate, sp02 levels and blood pressure and comes with a built-in AI to detect your mood (happy, sad, angry or emotional) and align it with therapeutic voice instructions to calm you down. These should include product descriptions, taglines, social media posts, email subject lines, and scripts- essentially everything a brand would need for a full-on marketing campaign.

                         Perplexity’s response to the content creation prompt

I really loved Perplexity’s response. The content was pretty on point and hit the trigger points very well. However, I felt that it mostly reiterated what I already mentioned in the prompt and didn’t have much originality.

Gemini pretty well highlighted the product's USPs, such as on-site therapeutic guidance and wearable wellness, explaining its strengths and benefits. It also created video frames within the scripts, which, according to me, was a winner for launch videos.

Winner: Gemini

3. Creative Writing

I asked both Perplexity and Gemini to craft a short dialogue (approx 200 to 300 words) between two characters who cannot directly state their feelings or the core issue between them. Both AI models delved into the poetic essence of the topic and crafted engaging dialogues that hooked me throughout. However, they differed in their execution style and content structure.

Prompt: Craft a short dialogue (approx. 200-300 words) between two characters who cannot directly state their true feelings or the core issue between them. Their entire conversation must rely on subtext, metaphor, and indirect allusions. Ensure the reader can perceive the underlying emotional tension and unspoken truths, despite the characters never articulating them explicitly.

                         Perplexity’s response to the creative writing prompt.

While Perplexity didn’t add scene visuals or poetic nuances, it did succeed in creating an abstract dialogue between two friends who talk about their strained relationship in the form of a garden. While it was absolutely heartfelt and engaging, in this task, Gemini showed a bit more poetic feel and creative flair than Perplexity.

Gemini’s response, namely “The Wilting Garden”, had me almost in tears. 

It was refreshing to read and draw parallels between this short dialogue and our real-life stories, which provides an interesting angle for the readers. The dialogue was sweet, easy to read, engaging, and poetic in its appearance.

Winner: Gemini

4. Coding

Coding test is the ultimate litmus test for AI chatbots, mostly because many early coders directly copy and paste the output code without running it through a manual compiling process. For this task, I thought a simple and responsive navigation bar for the frontend UI would be the best. 

I instructed the AI tool to focus on code usability, responsiveness, and UI friendliness while automatically debugging the code at runtime to eliminate errors or leaks.

Prompt: Can you write HTML, CSS, and JavaScript code snippets to create a user-friendly and responsive navigation bar for my website?                              

                         Perplexity's response to the coding prompt for web nav bar

I love how Perplexity generated three different scripts for HTML, CSS and JavaScript files and added a disclaimer on the code being just a "sample" for the user. Not just that, it also gave a integrated code editor environment to debug, execute, compile and run code successfully.

                                Gemini’s response to coding a web nav bar 

For Gemini, I used Google AI Studio, which offers a live integrated preview of your HTML and CSS code in an integrated data environment. To view the live preview of the navigation bar, I simply had to copy and paste the code as an HTML file and run it on my browser.

While both Gemini and Perplexity generated factually accurate, responsive, and user-friendly code snippets, Gemini also analyzed the utility of classes and functions.

Both Gemini and Perplexity excelled in generating complete, functional code snippets. What’s more, they offered a clear and practical starting point for your web development projects.

Winner: Split; Perplexity for ease of code and code continuation, Gemini for elaborating on function and class declarations.

5. Aggregating multi-source information

Both Perplexity and Gemini offer exceptional web browsing capabilities that help with aggregating multi-source information for user queries. Aggregating multiple sources isn’t just a form of information retrieval, it requires a special degree of synthesis, critical evaluation, and nuanced understanding drawn from disparate or conflicting sources.

I asked both Perplexity and Gemini to trace the evolution of public and academic discussions around the four-day work week over the last 10 years (2015 - 2025). Identify key arguments for and against it as they emerged, noting any significant real-world trials and their reported outcomes. Conclude by summarizing the current prevailing sentiment or points of debate, citing specific examples or data points from different regions or industries where possible. Present your findings in a chronological overview with distinct arguments and their counterpoints.

Prompts: Trace the evolution of the public and academic discussion around the four-day work week over the last 10 years (2015-2025). Identify key arguments for and against it as they emerged, noting any significant real-world trials or studies and their reported outcomes. Conclude by summarizing the current prevailing sentiment or points of debate, citing specific examples or data points from different regions or industries where possible. Present your findings as a chronological overview with distinct arguments and their counterpoints.

                                Perplexity’s response to the multi-source information aggregation prompt.

What I loved about Perplexity’s response was that it pulled the arguments from news pieces, articles, research papers, and carefully crafted the for and against arguments in a year-wise format. It was easily interpretable and gave more structure to the debate.

Also, Perplexity cited 8 overall sources and pulled insightful metrics that align with user perception of a 4-day work week, which in my case was a winner!

         Gemini’s response to the multi-source information aggregation prompt

Here is what I noticed: Gemini likely stood out more due to its deeper narrative exploration of the evolving arguments and more comprehensive discussion of regional/industry nuances and specific trial outcomes over time. 

However, Perplexity’s inclusion of recent statistics and legislative information offers a valuable snapshot of current adoption and policy discussions, complementing Gemini’s narrative focus. Both are a win-win in their own ways.

Winner: Split: Perplexity (for stat-based approach) and Gemini (for accurate narrative bend)

6. Deep research

As part of the recent upgrade to the models, AI chatbots now claim to handle complex research queries, meaning that they can go through tons of web resources for you. I aimed to put this to the test with an advanced research prompt that you can find in the PDF attached at the end of this task.

                                   Perplexity’s response to the deep research prompt.

Right off the bat, I noticed how cleanly and analytically Perplexity generated the introduction and followed it into the research objectives of that proposal. While my research question didn’t explicitly mention the presence of an independent and shared variable, it is evident that Perplexity browsed high-quality and accurate case studies and derived the correlation between variables, evidently in the objective section. It helped make my task extremely easy and convenient. 

However, it fell short on research design; it didn’t explore research methodologies, risks, and other good stuff.

                             Gemini’s response to the deep research prompt.

Where Gemini stood out was in the foreword. It started by searching for literature reviews, meta-analyses, and comprehensive reports discussing lawsuits against AI companies. That, according to me, is an early indication that your research proposal is headed in the right direction.

Another standout factor is that Gemini crafted an entire research proposal (which can be used with minor tweaks, AP edits, and content refinements) as legitimate research to pitch to a startup investor. I was so overwhelmed with Gemini’s response that I ended up working on the research proposal as an independent project for my next side hustle gig.

Winner: Gemini

If you’re interested in knowing more about the research proposals both these chatbots created as an outcome of a deep case study analysis, click here.

7. Analyzing academic papers 

Be it crafting a research proposal, extracting key insights from existing academic papers, or referencing accurate citations, both Gemini and Perplexity stood out to me and crunched qualitative or quantitative data within seconds. 

I also want to call out the “research” and “deep research” features of both of these AI tools. These features focus on AI-powered search engines that scour the web for information in real time and synthesize findings into concise answers with cited sources. 

I gave both ChatGPT and Perplexity a research paper on “Attention Is All You Need” and asked them to compare “attention mechanism” and “self-attention” to check how they can be different and put the comparison in a table.

Prompt:

Analyze the research paper as follows: “https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf“

Now that you’ve analyzed it, based on this research paper, try to compare the attention mechanism and self-attention, and put your findings in a table.

Perplexity’s response was extremely succinct and to the point. It extracted key details from the research paper pretty fast and offered a structured view of the comparison I wanted.  It also segregated the pointers based on multiple aspects (something I hadn’t prompted it to do).

The comparison pointers were well labeled and made it easy to understand the stark difference between two popular machine learning methods of content generation.

 
While Gemini banked on explaining the technical parameters, I found it a little difficult to interpret. Although it extracted relevant information and dissected the intent quite well, it might be a little difficult to comprehend for a beginner-level analyst who wants to learn more about these technical concepts.

Winner: Perplexity.

8. Multi-chat coherence 

Both Gemini and Perplexity maintain a full chat coherence primarily by utilizing a context window, which stores a limited history of ongoing conversations. No matter how far back you are in the chat, it would still retain the context and sentiment from earlier or previous messages. 

To check the multi-chat coherence of Perplexity and Gemini, I tried setting up a game with Gemini known as the quirky gadget combo challenge. 

                            Gemini’s response to multi-chat coherence

After storing the value of the first innovation and locking it in, I went for the second innovation, so that Gemini has a choice later in the game when I frame a particular scenario.

                                  Gemini’s response to multi-chat coherence

Finally, I created a fun situation that included applications of both these innovations and asked him to make sense of what was happening.

We can see that Gemini retained the applications of both the innovations that I had created earlier in the article, and was able to retrieve the exact function and the “why” behind those functions.

This suggests that Gemini could easily retain the context of two specific entities throughout the chat, also known as multi-chat coherence.

Similar to how Gemini reacted, Perplexity could also retain the context of both the innovations and explain the exact scenario in a detailed and structured format, while offering a strong multi-chat coherence quotient and contextual understanding of technical scenarios. 

Winner: Split: Perplexity and Gemini both retained context window.

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

Key insights on Perplexity vs. Gemini based on G2 Data

I looked at review data on G2 to find strengths and adoption patterns for Perplexity and Gemini. Here's what stood out:

Satisfaction ratings

• Perplexity excels in ease of use (95%), meets requirements (91%), and ease of setup (97%).
• Gemini excels in ease of use (94%), ease of doing business with (95%), and ease of setup (98%).

Industries used

• Perplexity dominates computer software, information technology and services, and marketing and advertising.
• Gemini dominates information technology and services, computer software, and marketing and advertising.

Highest-rated features

• Perplexity excels in complex query handling (88%), user interaction learning (87%), and natural conversation (87%)
• Gemini excels in natural conversation (92%), reliability (91%), and software integration (90%)

Lowest-rated features

• Perplexity struggles with API flexibility (74%), software integration (77%), and error learning (80%)
• Gemini struggles with Error learning (86%), API flexibility (86%), and customizability (87%)

Perplexity vs. Gemini: Frequently asked questions (FAQs)

1. Which platform offers more accurate and up-to-date responses: Perplexity or Gemini?

Perplexity stands out for real-time web search integration and transparent source citations, making it ideal for users who value up-to-the-minute accuracy. Gemini, powered by Google’s ecosystem, also offers high-quality responses but may rely more on model knowledge than live web updates, depending on the context.

2. Which tool is better suited for business or professional use cases: Perplexity vs Gemini?

Perplexity Pro is optimized for researchers, analysts, and knowledge workers who require deep web-backed responses with minimal hallucination. Gemini Pro integrates more seamlessly with Google Workspace (Docs, Sheets, Gmail), making it a better fit if your team is already in the Google ecosystem.

3. What are the pricing differences, and which one gives better value for money, Perplexity vs Gemini?

Perplexity Pro is competitively priced at around $20/month, offering unlimited Pro searches, advanced models (like GPT-4-turbo), and web access. Gemini Advanced, part of Google One AI Premium ($19.99/month), includes Gemini 1.5 Pro with expanded context windows and tight Google ecosystem perks. If web-based research is critical, Perplexity offers more focused value. If you're deep in Google Workspace, Gemini might give you more utility.   

4. How do the customization and integration options compare in Perplexity and Gemini?

Perplexity offers limited customization and integration options, mainly focusing on a clean, AI-powered Q&A experience without deep enterprise-level tooling. In contrast, Gemini (especially Gemini 2.5 Advanced and Gemini for Workspace) provides broader integration with Google products and more flexible customization through Vertex AI and Google Cloud tools.

5. How do Perplexity and Gemini handle data privacy and user security?

Gemini inherits Google’s enterprise-grade security and data management protocols, including robust admin controls for business users. Perplexity is more transparent about its data sources and offers anonymous browsing modes, but its privacy policies may not yet match Google’s enterprise compliance standards. For regulated industries, Gemini may be the safer bet, though Perplexity is gaining traction among users who value source transparency and minimal data tracking.

The end verdict: Which AI chatbot would you chat with?

When I glance over the outcomes of all eight tasks, I see Perplexity has its own set of strengths, and so does Gemini. The success of an AI chatbot will depend on the type of goal you want to achieve. For an academician or student, Gemini might offer better explanations of scholarly concepts, but, similarly, for a content writer, Perplexity might be more concise.

Although both of these tools have their pluses, Gemini stood out in three tasks, each catering to the marketing flair, nuanced creative flow of speech, and argument accuracy. Perplexity, on the other hand, won for two tasks, each aligned with the purpose of content marketing or academic writing.

So, given the subjectivity of content and the adaptability of users for a particular chatbot, the decision of Gemini vs. Perplexity depends on your purpose, project bandwidth, and eye for detail. 

What I’ve inferred about both these tools also aligns with what G2 reviews say about them, and if you want to get started on your own, maybe this comparison can help.