Perplexity vs ChatGPT: Features, differences, and real-world performance

What is ChatGPT?

ChatGPT is OpenAI's conversational AI chatbot that generates text, writes code, analyzes images, and creates visual content. It powers everything from quick writing tasks to complex coding projects. The tool processes multimodal inputs, including text, images, and documents, and delivers responses in natural language.

Your teams can use ChatGPT to draft reports, debug code, analyze spreadsheets, or brainstorm campaign ideas. The latest advanced AI models (GPT-5.2 and GPT-5 mini) handle complex reasoning tasks. They break down multi-step problems and provide detailed explanations. ChatGPT doesn't search the web by default. It works from its training materials and uploaded files. Despite its strengths, ChatGPT still operates within a defined knowledge cutoff unless web browsing is enabled, which can affect how up-to-date its answers feel for time-sensitive queries.

What is Perplexity?

Perplexity is a real-time AI-powered answer engine that searches the web and cites sources. It's built for research and information gathering. Every response includes citations that link directly to original sources. Perplexity positions itself as an AI search engine and answer engine in its own right, leveraging AI to transform real-time web access into concise, cited responses.

You get instant access to current information: stock prices, news, research papers, and technical documentation. Perplexity searches multiple sources simultaneously and synthesizes findings into coherent and accurate answers. The tool specializes in fact-finding and exploration. It's designed for users who need verified, up-to-date information with transparent sourcing.

ChatGPT and Perplexity features comparison

Both platforms belong to a new generation of AI chatbots that blur the line between conversational assistants and productivity tools. Unlike earlier AI chatbots that focused on simple Q&A, both ChatGPT and Perplexity aim to support complex workflows that span research, creation, and decision-making. Still, they differ in how they handle search results, real-time data, and reasoning depth. Both tools offer free plans with limited features and paid subscriptions that unlock advanced models and capabilities. 

This table compares Perplexity AI vs ChatGPT capabilities across free and paid tiers. It shows you exactly what each platform offers as an AI assistant for different use cases.

The core distinction is that ChatGPT generates content and solves problems using its knowledge base. Perplexity retrieves and synthesizes information from live web sources. This architectural split becomes especially visible in real-world use, where one system focuses on reasoning and creation while the other prioritizes pulling data from live sources.

Data collected: December 2025

Differences between Perplexity AI and ChatGPT

The tools diverge fundamentally in architecture and primary emphasis. ChatGPT is generation-first, optimized for creating original content, while Perplexity AI is retrieval-first, designed to ground responses in external sources and present synthesized answers with transparent citations.

Both tools offer free versions that allow users to experiment with advanced models, making it easier to compare ChatGPT and Perplexity side by side before committing to a paid plan.

Search-first vs generation-first architecture: Perplexity AI searches the web by default for most queries and automatically grounds responses in retrieved sources. ChatGPT generates responses primarily from its model knowledge and conversation context, and accesses the web only when real-time search or web browsing is explicitly enabled. This difference shapes how each tool approaches questions and uncertainty. Because Perplexity retrieves information dynamically, its search results tend to reflect current events more reliably than ChatGPT’s default responses.

Citation and transparency: Perplexity AI provides persistent, numbered citations that link directly to original sources, making verification straightforward. ChatGPT can provide source links when real-time web search is enabled, but citations are less consistently inline and are not as central to the user interface.

Real-time information access: Perplexity offers access to current information by default, including recent news, market data, and newly published research. ChatGPT relies on its model knowledge unless real-time web search is enabled, at which point it can also retrieve up-to-date information.

Content creation capabilities: ChatGPT excels at generating original content such as articles, code, creative writing, and marketing copy, with strong support for iteration and style control. Perplexity can generate original text as well, but its primary strength is research-driven synthesis rather than long-form creative content from scratch.

Code execution and data analysis: ChatGPT supports direct Python code execution, file uploads (such as CSVs), data analysis, and visualization within the user interface. Perplexity AI can explain code, analyze files, and provide examples, but its execution and data-analysis capabilities are more limited.

Model flexibility: Perplexity Pro offers access to multiple frontier models, including OpenAI and non-OpenAI options such as Claude, within a single subscription. ChatGPT provides access to multiple OpenAI models but is limited to OpenAI’s model ecosystem.

Conversation memory: ChatGPT supports optional long-term memory and preference learning across conversations, enabling more personalized interactions. Perplexity AI treats interactions as more search-oriented and session-based, prioritizing fresh retrieval over persistent personalization.

Similarities between Perplexity AI and ChatGPT

Despite their key differences, both platforms share a set of core capabilities that make them accessible and practical for everyday use as an AI assistant. These shared features are especially relevant when evaluating user adoption and potential enterprise deployment.

Multimodal input processing: Both tools can process text and uploaded files, including documents and images, and answer questions about them. You can upload a chart or document and receive contextual analysis from either platform, though the depth and tooling vary. ChatGPT’s voice mode and advanced voice mode enable hands-free interaction, while Perplexity focuses on fast, text-based research workflows.

Mobile and web accessibility: Both platforms are accessible via web browsers and dedicated mobile apps on iOS and Android. Conversations and account data sync across devices within each platform’s ecosystem.

Image generation: Both platforms support text-to-image generation. ChatGPT offers image generation using OpenAI’s image models (such as DALL·E). Perplexity Pro provides access to multiple image-generation models, depending on plan and availability.

Conversational interface: Both rely on natural-language conversation rather than rigid commands. Users can ask follow-up questions, refine prompts, and iterate within a conversational flow.

Enterprise offerings: Both offer team or enterprise-oriented plans designed for business use, with features such as centralized billing, access controls, and additional security or compliance options, though the scope and maturity of these features differ.

API availability: Both ecosystems support developer integration through APIs offered under separate pricing and usage terms, enabling incorporation into custom applications and workflows.

Free-tier availability: Both platforms offer free-access tiers that allow users to explore core functionality before upgrading to a paid plan, with usage limits and feature restrictions. For a small business owner, these free tiers are often sufficient to test workflows, evaluate accuracy, and decide which AI assistant fits daily operations best.

Perplexity AI vs ChatGPT: Accuracy comparison in practice

We compared ChatGPT Plus (5.2) with web access and Perplexity Pro. Features and specifications tell part of the story. Real-world performance reveals the rest. We tested both tools on identical tasks across coding, research, content creation, and analysis.

We tested Perplexity as an AI-powered search engine, which can engage in human-like conversations, and ChatGPT as one of the major AI chatbots available with a free forever plan. We tested Perplexity extensively alongside ChatGPT to see how each tool performs when accuracy depends on real-time information rather than static training data.

Each test used the same prompt for both platforms. We evaluated responses on factual accuracy, completeness, usability, and time to result. We included an assessment of which tool performed better for each specific use case. You can also compare free and paid ChatGPT.

Coding

The prompt: "Write a Python function that takes a list of dictionaries containing product sales data (product_name, quantity, price) and returns a summary showing total revenue by product, sorted by revenue descending. Include error handling for missing or invalid data."

Why we tested this: Coding tasks require precise syntax, logical structure, and practical error handling. This prompt tests both tools' ability to write production-ready code with real-world considerations.

ChatGPT result: Delivered clean, production-ready code with strict validation and explicit error handling. The function enforced required fields, validated data types, rejected negative values, and surfaced clear, index-based error messages when encountering invalid input. The output format was well-suited for APIs or reporting, and documentation was thorough with clear docstrings. The approach favored data integrity by failing fast on bad input.

Perplexity result: Provided functional and pragmatic code that emphasized resilience over strictness. Invalid or malformed records were safely skipped, allowing valid data to be processed without interruption. The solution handled numeric string coercion and non-dictionary inputs gracefully, but documentation was lighter, and errors were silently ignored rather than reported. The output was compact and idiomatic, though less self-describing.

Winner: ChatGPT. While both Perplexity and ChatGPT’s solutions were correct for the same query, ChatGPT’s implementation demonstrated stronger production-oriented discipline through explicit validation, clearer error signaling, and more comprehensive documentation. 

Perplexity’s search engine approach was flexible and robust for messy data, but ChatGPT offered greater transparency and control, which are key advantages in professional engineering contexts. In these scenarios, ChatGPT feels more like an interactive development environment than a traditional chatbot, especially when executing code or analyzing files.

Deep research

The prompt: "Research the current state of sodium-ion battery technology for grid-scale energy storage. Compare it to lithium-ion alternatives, identify key manufacturers, and explain recent breakthroughs in the past 6 months."

Why we tested this: Deep research requires finding current information from multiple authoritative sources, synthesizing complex technical content, and identifying recent developments. This tests information retrieval and synthesis capabilities.

ChatGPT result: Delivered a comprehensive, deployment-focused analysis of sodium-ion batteries for grid-scale storage. The response clearly compared sodium-ion with lithium-ion (especially LFP) across cost, safety, maturity, and bankability, and identified key manufacturers with active grid relevance. Recent breakthroughs were framed around concrete commercial milestones, such as GWh-scale projects, US grid pilots entering operation, and multi-GWh supply agreements, providing strong insight into what has changed in the last six months from a market and adoption perspective.

Perplexity result: Provided a well-structured and citation-rich overview emphasizing technology fundamentals, manufacturer lists, and recent scientific and materials advances. While sources were explicit and easy to verify, many cited “breakthroughs” were research-stage or adjacent sodium technologies, with less emphasis on grid-scale deployments, operating data, or commercial impact within the last six months.

Winner: ChatGPT. While Perplexity excels at source aggregation and academic-style citation, ChatGPT demonstrated stronger synthesis and judgment by anchoring recent developments to real-world grid deployments, commercial traction, and bankability considerations. For evaluating the current state of sodium-ion batteries in grid-scale energy storage, ChatGPT’s analysis was more decision-relevant and practically grounded.

Content creation

The prompt: "Write a 300-word blog post introduction for a SaaS company explaining how AI-powered contract analysis reduces legal review time. Target audience: procurement directors at mid-market companies."

Why we tested this: Content creation requires understanding audience, tone, persuasive structure, and domain knowledge. This tests creative writing with a specific business context to see if ChatGPT and Perplexity can generate human-like text using large language models.

ChatGPT result: Generated a well-structured, professional introduction that clearly articulated procurement challenges and positioned AI-powered contract analysis as a practical solution. The tone was measured and credible, with smooth transitions and a strong focus on consistency, risk control, and operational efficiency. The copy worked well as educational or thought-leadership content and would require minimal polishing for publication.

Perplexity result: Delivered a sharper, more persuasive introduction with a strong opening hook and language that closely matched how procurement directors describe their day-to-day frustrations. The copy used concrete examples, bullet-point takeaways, and forward-looking promises to build momentum. It read more like product marketing content, clearly signaling ROI and encouraging readers to continue.

Winner: Tie. Creative content generation is ChatGPT's core strength. The output required minimal editing and captured the right tone immediately. While the outputs of Perplexity and ChatGPT were high quality, Perplexity’s version showed a strong audience resonance and marketing impact. Its direct tone, specificity, and clear value framing make it more compelling for a SaaS blog aimed at engaging busy procurement leaders and driving interest in the product.

Creative writing

The prompt: "Write the opening paragraph of a science fiction short story where an AI archaeologist discovers evidence of a previous advanced civilization on Mars that humans don't know about."

Why we tested this: Creative writing tests imagination, narrative voice, atmospheric detail, and engaging prose. This prompt requires world-building and dramatic tension.

ChatGPT result: Delivered a restrained, atmospheric opening that focused on internal logic and discovery rather than spectacle. The prose was clean and suggestive, establishing the AI’s perspective and the sense of a hidden Martian history through implication and tone. The final line provided a strong conceptual hook while leaving narrative space for the story to expand.

Perplexity result: Perplexity AI produced a vivid, detail-heavy opening with strong visual and scientific specificity. The paragraph immediately raised the stakes through concrete imagery, precise locations, and explicit revelations, creating a cinematic sense of mystery. The writing emphasized immediacy and scale over subtlety.

Winner: Tie (style-dependent). ChatGPT’s version excels in subtle tension and thematic restraint, while Perplexity’s opening is more dramatic and visually arresting. The stronger choice depends on whether the story aims for a slow-burn, introspective tone or an immediate, high-impact revelation.

Image generation

The prompt: "Create an image of a modern minimalist office workspace with a large window showing a city skyline at sunset, clean desk with a laptop, warm lighting, photorealistic style."

Why we tested this: Image generation tests visual interpretation of text descriptions, attention to detail, and artistic quality. This prompt requires balancing multiple specific elements.

ChatGPT result: Generated a photorealistic image using DALL-E 3 that matched all specified elements. It’s the model designed for photorealistic and stylistic image synthesis based on detailed text prompts, which is why it works well for scenes like a modern minimalist office with realistic lighting and materials.

Perplexity result: This image was generated using Perplexity’s “Default” image generation setting, which automatically selects one of the available models (GPT Image 1, Nano Banana, Seedream, or FLUX.1) based on the request. The exact model chosen is not exposed per individual image.

Winner: Tie. Both platforms delivered high-quality images. ChatGPT offers straightforward DALL-E 3 generation. Perplexity Pro selects one of the available AI models.

Image analysis

The prompt: We uploaded a complex infographic showing the conceptual robotic technique's relative capability.

"Analyze this infographic and extract the key insights. Which product technique is the strongest? What conclusions are evident?"

Why we tested this: Image analysis tests visual interpretation, data extraction from complex graphics, and synthesis of multiple data points. This simulates real business use cases.

ChatGPT result: Produced a structured, insight-driven analysis that went beyond surface-level description. ChatGPT’s response clearly identified foundation models as the strongest technique, highlighted where they outperform classical and traditional ML approaches, and explained why perception and human–robot interaction advance faster than physical control. The analysis emphasized implications and constraints, treating the infographic as an input for strategic reasoning rather than just visual summarization.

Perplexity result: Delivered a careful, category-by-category interpretation closely aligned with the visible chart elements. The analysis accurately described trends across mobility, dexterity, perception, and interface domains, but focused more on descriptive pattern recognition and less on broader synthesis or strategic conclusions.

Winner: ChatGPT. While both analyses were accurate, ChatGPT demonstrated stronger reasoning by translating visual data into higher-level insights about technological bottlenecks and paradigm shifts. This made the output more actionable for business and product decision-making contexts.

Information gathering

The prompt: "What are the current FDA approval requirements for AI-based medical diagnostic tools? Include recent regulatory changes from 2024."

Why we tested this: Information gathering requires finding authoritative sources, synthesizing regulatory details, and identifying current requirements. This tests research capabilities with specialized domain knowledge.

ChatGPT result: Delivered a broad, well-structured overview of FDA regulation for AI-based diagnostics, covering core approval pathways, safety and effectiveness expectations, and lifecycle considerations. ChatGPT’s response highlighted key 2024–2025 developments such as Predetermined Change Control Plans (PCCPs), transparency guidance, and the FDA’s Total Product Lifecycle approach, framing them in an accessible, high-level narrative suitable for product or business audiences.

Perplexity result: Provided a more granular, compliance-oriented synthesis with explicit citations to FDA guidance, legal analyses, and peer-reviewed sources. Perplexity’s response clearly distinguished between device and non-device clinical decision support, detailed validation and post-market requirements, and precisely contextualized 2024 regulatory changes. The inclusion of practical implications (e.g., when PCCPs are expected, how transparency affects risk classification) made the analysis particularly useful for regulatory and legal stakeholders.

Winner: Perplexity. While ChatGPT offered a strong conceptual overview, Perplexity AI excelled in regulatory precision, traceability, and depth. For teams preparing FDA submissions or navigating evolving compliance requirements for AI diagnostic tools, Perplexity’s citation-rich, detail-focused approach provides greater confidence and immediate practical value.

Data analysis

The prompt: We uploaded a CSV file containing 6 months of customer support ticket data (ticket ID, category, priority, resolution time, customer satisfaction score). ChatGPT generated 500+ rows of data to reflect the actual file for analysis.

"Analyze this support ticket data. What's the average resolution time by category? Which categories have the lowest satisfaction scores? Create a visualization showing the relationship between resolution time and satisfaction."

Why we tested this: Data analysis requires processing structured data, performing calculations, identifying patterns, and creating visual representations. This tests analytical and computational capabilities for practical business intelligence.

ChatGPT result: Successfully loaded and analyzed the dataset end-to-end. ChatGPT’s response calculated average resolution times and satisfaction scores by category, identified underperforming areas, and produced a clear visualization illustrating the negative correlation between resolution time and customer satisfaction. Beyond calculations, it provided interpretive insights explaining why certain categories perform poorly and what operational factors may be driving those results.

Perplexity result: Encountered schema interpretation issues and was unable to complete a full analysis. Perplexity’s response focused on outlining how the analysis could be done, providing example code and guidance once column names were corrected. While technically accurate, it stopped short of producing actual results or visual output from the dataset.

Winner: ChatGPT. By directly executing the analysis, generating metrics, and visualizing the relationship between key variables, ChatGPT delivered immediate, decision-ready insights. Perplexity’s guidance was useful from an instructional standpoint, but ChatGPT’s ability to operate on the data itself made it far more effective for real-world analytical tasks. When it comes to AI for data analysis, ChatGPT's capabilities are essential. 

Web search

The prompt: "What were the major announcements from AWS re: Invent 2024? Focus on new AI and machine learning services."

Why we tested this: Web search tests the ability to find recent, specific information from a particular event and synthesize multiple announcements into a coherent summary.

ChatGPT result: Delivered a broad, executive-level overview of AWS re:Invent 2024’s AI and ML announcements. ChatGPT's response captured the major themes: generative AI expansion, Bedrock growth, SageMaker unification, and new AI infrastructure, and explained how they fit into AWS’s overall strategy. It was accessible and well-suited for readers seeking a high-level understanding of where AWS is investing in AI.

Perplexity result: Provided a more detailed, launch-by-launch breakdown with precise naming, feature-level descriptions, and extensive citations. The response clearly distinguished between foundation models, chips, Bedrock capabilities, Amazon Q enhancements, and SageMaker updates, offering concrete examples (e.g., guardrails, model distillation, agent orchestration) that made the announcements easier to evaluate from a technical or product-planning perspective.

Winner: Perplexity. While ChatGPT offered a strong strategic summary, Perplexity’s depth, specificity, and citation-backed structure made it more effective for understanding the full scope of AWS’s AI and ML announcements. For readers tracking platform capabilities or assessing competitive impact, Perplexity’s detail-oriented approach provides greater clarity and confidence. This test highlights why Perplexity functions as a dedicated answer engine rather than a general-purpose chatbot, excelling when fresh sources matter most.

Video generation

The prompt: "Create a 5-second video showing a product reveal: a smartphone rising from a pedestal with dramatic lighting and camera rotation."

Why we tested this: Video generation tests emerging AI capabilities in multimedia content creation.

ChatGPT result: Does not offer video generation capabilities. The platform focuses on text, images, and code. It generated an image instead.

Perplexity result: Perplexity Pro generated a video with visible malformation of a smartphone.

Winner: Perplexity. Video generation is available only in Perplexity Pro. However, the video generated by Perplexity AI encounters typical AI problems: malformed objects and unnatural animations. For video content, you'll need specialized platforms like Runway, Pika, or OpenAI's Sora (when available).

Perplexity vs ChatGPT in enterprises

Business AI deployment requires more than powerful features and a simple AI assistant. You need reliable integration, security controls, and tools that solve specific departmental challenges. Here's how Perplexity AI and ChatGPT perform in real enterprise contexts.

Both tools offer enterprise plans with SSO, admin dashboards, and usage analytics. But their core architectures create different strengths for various business applications. At scale, enterprises often deploy both tools together, using ChatGPT for generation and Perplexity for pulling data with verifiable sources.

For comprehensive enterprise AI deployments that require unified governance across multiple AI tools, consider platforms like nexos.ai that provide centralized management, access controls, and usage tracking across 200+ AI models, including both ChatGPT and Perplexity AI. This approach gives your teams the right tool for each task while IT maintains complete visibility and control. See how this compares in our detailed nexos.ai vs Perplexity Enterprise Pro analysis.

Customer support

ChatGPT strengths: Generates personalized responses to customer inquiries. Your support team drafts responses faster by describing the situation and letting ChatGPT produce appropriate customer-facing language. Handles tone adjustment: from apologetic to professional to friendly, based on context. Creates knowledge base articles and provides AI assistance with documentation quickly.

ChatGPT weaknesses: Doesn't access your internal support tickets, CRM data, or knowledge base without integration or retrieval tools. Can't verify current product information or policies without you providing that context explicitly.

Perplexity strengths: Quickly researches product specifications, competitor solutions, and technical documentation when support agents need verified information. Finds recent bug reports, patch notes, and resolution procedures from public sources.

Perplexity weaknesses: Can't access your internal systems or generate the empathetic, personalized responses customers expect. Better for agent research than customer-facing communication.

Best choice: ChatGPT for response drafting and documentation. Perplexity AI for technical research and verification. Many enterprise support teams use both. ChatGPT to write responses, Perplexity to fact-check technical claims.

Sales and marketing

ChatGPT strengths: Creates sales enablement materials: pitch decks, one-pagers, case study drafts, and email campaigns. Generates personalized outreach at scale. Your sales team describes a prospect's situation and gets tailored messaging instantly. Develops blog posts, social media content, and AI for marketing materials that match your brand voice. Automates repetitive content tasks that drain marketing team time. See how AI in marketing automation transforms these workflows.

ChatGPT weaknesses: Doesn't know current market trends, competitor announcements, or recent industry developments unless you provide them. With real time web search enabled, ChatGPT can access current trends and announcements. Can hallucinate statistics or claims that sound credible but aren't verified.

Perplexity strengths: Researches competitors, market trends, and industry developments with cited sources. Your marketing team gets current data for campaigns: recent product launches, pricing changes, and customer reviews. It gathers intelligence before major pitches or campaign planning.

Perplexity weaknesses: Doesn't generate polished marketing copy. Better for research than content creation. Won't match your brand voice without significant editing. It is less refined and less brand-sensitive than ChatGPT.

Best choice: ChatGPT for content creation and personalization. Perplexity AI for competitive intelligence and market research. Effective sales and marketing teams combine both. Perplexity AI for research and data gathering, ChatGPT for creating customer-facing materials.

Data analysis and reporting

ChatGPT strengths: Processes uploaded datasets directly. Your analysts upload CSV or Excel files and get immediate statistical analysis, visualizations, and insights. Writes Python code to clean data, perform calculations, and generate charts. Creates executive summaries that translate complex data into business recommendations.

ChatGPT weaknesses: Limited to files you upload manually. Doesn't integrate with your data warehouse, BI tools, or live dashboards without custom integration via APIs.

Perplexity strengths: Researches industry benchmarks and comparative data from public sources. Finds market research reports, analyst predictions, and industry statistics to contextualize your internal data.

Perplexity weaknesses: Perplexity AI can inspect uploaded files, but performs only limited analysis. It cannot execute arbitrary code or do advanced statistical computation.

Best choice: ChatGPT for actual data analysis. The code execution capability is essential. You can't analyze data without computational tools. Perplexity AI serves as a research supplement when you need external benchmarks or industry context to frame your internal findings. ChatGPT is decisively better for hands-on data analysis.

IT helpdesk and technical support

ChatGPT strengths: Generates step-by-step troubleshooting guides tailored to specific scenarios. Your IT team describes an issue and gets detailed resolution procedures. Creates documentation for common problems. Writes scripts for automation tasks—password resets, user provisioning, system checks. Provides AI assistance to junior technicians by explaining technical concepts clearly.

ChatGPT weaknesses: Doesn't know your specific systems, configurations, or internal procedures without explicit context. Can't verify if suggested solutions match your security policies or infrastructure setup.

Perplexity strengths: Researches known issues with specific software versions, hardware models, or configurations. Finds official vendor documentation, community forum solutions, and recent bug reports. Your technicians get cited sources they can trust when dealing with unfamiliar systems.

Perplexity weaknesses: It can generate custom scripts or documentation, but the outputs are usually less tailored and less ready for execution. Can't adapt general solutions to your specific environment without additional work.

Best choice: Both AI tools serve different functions. ChatGPT for creating documentation, writing scripts, and generating custom troubleshooting guides. Perplexity AI for researching unfamiliar issues and finding vendor-specific solutions. IT teams benefit from using ChatGPT for internal documentation and Perplexity AI for external research.

Perplexity AI vs ChatGPT: Which is better?

Neither of the two AI tools is universally superior. Each excels at fundamentally different tasks. Your choice depends on what you're trying to accomplish. The decision ultimately depends on whether you value creative reasoning within a known knowledge cutoff or continuous access to live, verifiable information.

Choose ChatGPT when you need:

• Original content creation: articles, code, marketing copy, creative writing,
• Data analysis and visualization from uploaded files,
• Personalized responses that maintain conversation context,
• Code execution and testing capabilities,
• Document creation and editing workflows,
• Tasks that benefit from conversation memory and custom instructions.

Choose Perplexity when you need:

• Current, verified information with source citations,
• Research across multiple authoritative sources,
• Real-time data: stock prices, news, recent events,
• Fact-checking and verification with transparent sourcing,
• Competitive intelligence and market research,
• Quick answers to factual questions where source credibility matters.

For enterprises: Most organizations benefit from both tools deployed strategically. Your content creators, developers, and analysts need ChatGPT's generation and execution capabilities. Your researchers, fact-checkers, and competitive intelligence teams need Perplexity's search and citation features.

The AI platform for business approach addresses this tool-fragmentation problem. Rather than managing separate subscriptions and access controls for multiple AI tools, platforms like nexos.ai provide unified governance across ChatGPT, Perplexity, and 200+ other AI models. Your IT team sets policies once. Your employees have access to the right tool for each task. You get complete visibility into usage, costs, and compliance across your entire AI deployment.

Consider your primary use case. Creating content? Choose ChatGPT. Finding information? Choose Perplexity. Need both capabilities organization-wide? Explore unified AI platforms that give your teams access to multiple tools through a single, governed interface.