What is the Definitive Comparison Between Perplexity Vs ChatGPT for Research and Creativity?
Large Language Models or LLMs have revolutionized how people interact with computers. These powerful Artificial Intelligence tools can generate human-like text, answer questions, and assist with complex tasks.
The two most prominent tools in this field are ChatGPT by OpenAI and Perplexity AI. Users frequently compare them to determine which one is better for their specific needs. This detailed comparison explores their core differences, features, and optimal use cases.
The key distinction lies in their fundamental purpose and design philosophy. ChatGPT is primarily a conversational AI and a creative content generator. It excels at engaging in long dialogues, brainstorming ideas, and producing creative writing. Its strength comes from its massive training dataset and its focus on language generation.
Perplexity AI, on the other hand, is an AI-powered answer engine. Its core design focuses on real-time information retrieval and verifiable sources. It functions more like a next-generation search engine that synthesizes answers with citations. This makes Perplexity an essential tool for research, fact-checking, and finding up-to-date information.
What are the Core Differences in How Perplexity and ChatGPT Work?
The way each tool generates its response is the most significant difference. ChatGPT uses its internal Large Language Models like GPT-4o, GPT-5, and their predecessors. It relies heavily on the vast amount of data it was trained on to formulate answers.
While ChatGPT can browse the web for current information, this is often a secondary function. Its core strength is its ability to reason, synthesize, and maintain a long conversation context.
Perplexity AI operates differently by default. When a user asks a question, Perplexity actively searches the live web using its models. It retrieves information from multiple sources and synthesizes a direct, concise answer. Crucially, Perplexity provides clear, clickable source citations with every response. This focus on sourcing minimizes the risk of hallucinations or presenting outdated facts.
Perplexity’s architecture is a hybrid model. It integrates various leading LLMs, including models from OpenAI (like GPT-4 and GPT-5 for Pro users),
Anthropic’s Claude, and Google’s Gemini. It also uses its own proprietary models like Sonar. This multi-model approach allows Perplexity to optimize the model used for each specific query. ChatGPT, by contrast, is locked into the OpenAI ecosystem.
How Does Perplexity Compare to ChatGPT in Terms of Information Sourcing and Accuracy?
Information sourcing is where Perplexity AI establishes a clear advantage for research-oriented tasks. Perplexity’s default operation is to find and cite multiple web sources for its answers. The result is a highly transparent output that allows users to verify the information easily. This transparency builds trust, which is vital for academic, journalistic, and professional work.
ChatGPT’s free version generally relies only on its pre-trained data, which has a knowledge cutoff date. While paid versions have web browsing capabilities, they do not always cite sources as prominently or by default. A user often needs to explicitly ask ChatGPT to perform a web search or request citations. Even with web browsing enabled, the synthesis is often more narrative than a structured summary with links.
Both tools can still produce errors, which the AI community calls hallucinations. However, Perplexity’s citation-first approach provides an immediate path to fact-checking. Researchers and students who require verifiable data often find Perplexity more reliable for initial information gathering. ChatGPT is improving its citation capability, but Perplexity was built with this feature as a foundational element.
Which is Better: Perplexity Pro vs ChatGPT Plus? A Detailed Feature Comparison.
Both Perplexity and ChatGPT offer premium subscription tiers at a similar price point, typically around $20 per month. These paid plans, Perplexity Pro and ChatGPT Plus, unlock a substantial upgrade in capabilities. The choice between them depends entirely on the user’s primary workflow.
ChatGPT Plus gives users priority access to the most powerful OpenAI models, such as GPT-4o and GPT-5. It offers faster response times and improved reasoning for complex tasks. A major feature is the ability to create Custom GPTs.
These are tailored versions of the AI, specialized with specific instructions and knowledge bases for repeatable tasks. Plus users also get access to integrated tools like DALL-E for image generation and Advanced Data Analysis for code execution and file processing. ChatGPT Plus is positioned as the all-in-one creative and conversational collaborator.
Perplexity Pro focuses on maximizing the power and depth of its research capabilities. Pro users gain access to a larger pool of advanced language models from various providers, including the latest GPT and Claude models. It offers “Pro Searches” which execute more complex, deeper, and more thorough web searches.
This feature is beneficial for synthesizing comprehensive reports from a wide range of academic or industry-specific resources. Perplexity Pro also allows for unlimited use of its “Copilot” feature, which guides the user to refine their queries for better results. Its primary value is in deep, verifiable research and fact-checking.
How Does Perplexity Compare to ChatGPT for Coding and Technical Tasks?
ChatGPT is generally considered the superior tool for complex coding and software development tasks. Its underlying models, especially GPT-4 and GPT-5, were trained on massive amounts of code. The Plus version includes a powerful Code Interpreter or Advanced Data Analysis feature.
This allows the AI to write, run, and test Python code within a sand boxed environment. It can perform multi-step debugging, analyze data files, and generate complete, functional scripts. ChatGPT’s ability to maintain context over long, multi-turn coding sessions is a significant advantage.
Perplexity can certainly assist with coding. It provides quick explanations of code snippets, helps with syntax, and offers debugging suggestions. It uses its excellent search capabilities to retrieve relevant, up-to-date coding documentation and examples.
However, Perplexity does not natively include an integrated code execution environment like ChatGPT Plus. For quick reference, learning new concepts, or finding immediate code examples, Perplexity works well. For complex project builds, large-scale debugging, or sophisticated data analysis, ChatGPT retains the edge.
Which is Better for Creative Writing and Content Generation: ChatGPT or Perplexity?
ChatGPT is the clear winner for tasks that require creativity, narrative flow, and human-like conversation. Its models are fine-tuned to excel at generating long-form content. This includes stories, poems, scripts, marketing copy, and detailed narrative explanations.
ChatGPT’s inherent strength is in its conversational fluidity and its ability to adopt different tones and styles easily. Users find its output more engaging and smoother for published content.
Perplexity is functional for content generation. It can certainly draft articles, emails, or summaries. However, its responses often lean towards a concise, factual, and research-oriented tone. The output may feel more like a structured, bulleted report rather than a flowing narrative.
While a Pro user can select a high-end model like GPT-4 to improve creativity, Perplexity’s interface and design prioritize information delivery over stylistic flair. For a creative professional, writer, or marketer, ChatGPT is generally the more versatile and powerful collaborator.
Does Perplexity Use ChatGPT’s Technology, and How is Perplexity Different from ChatGPT?
This is a common question, and the answer has become more nuanced over time. Perplexity AI is a distinct company with its own core technology and proprietary models, such as its Sonar models. Therefore, Perplexity does not rely entirely on or belong to the same company as ChatGPT.
However, Perplexity Pro does integrate and offer access to OpenAI’s Large Language Models, which are the same models that power ChatGPT. For a Perplexity Pro subscriber, they can choose to use GPT-4 or GPT-5 for their queries. This means a Perplexity user can utilize the core technology of ChatGPT within the Perplexity environment. This integration allows Perplexity to leverage the reasoning and creative strengths of OpenAI’s models.
The fundamental difference remains in their application. ChatGPT is a chat-first, conversational AI that uses its models for generation and reasoning. Perplexity is an answer-engine-first product that uses its models to search, synthesize, and cite. Perplexity’s core distinction is its transparency and mandatory citation process. Even when using a ChatGPT model within Perplexity, the final output is formatted and sourced in the Perplexity style.
When Should People Use Perplexity Versus ChatGPT in Their Daily Workflows?
Choosing the right tool depends on the job at hand. For tasks requiring immediate, verifiable facts and up-to-date information, people should use Perplexity. This applies to journalists fact-checking an article, students writing a research paper with required sources, or professionals needing quick market data.
Perplexity excels at quickly synthesizing information from the live web and providing a concise, accurate answer with links. Its focus is on minimizing the information gap and providing truth and clarity.
For tasks that demand extensive text generation, complex problem-solving, or long-term conversation, people should use ChatGPT. This includes content creators drafting a blog post, software developers writing and debugging code, or users looking for a brainstorming partner.
ChatGPT is also better for tasks requiring creative adaptation, such as role-playing or generating various versions of a concept. Its strength is its ability to remember context and generate highly coherent, narrative text.
Many advanced users adopt a synergistic approach. They use Perplexity first for research, gathering facts, and collecting sources.
Then, they take that verified information and use ChatGPT to transform it into polished, creative, or structured final content. This combined workflow maximizes the strengths of both platforms, leading to high-quality, well-sourced output.
How Do the User Interfaces and User Experience of Perplexity and ChatGPT Differ?
The user interfaces of Perplexity and ChatGPT reflect their core design philosophies. ChatGPT offers a clean, straightforward chat interface. It is designed for conversational flow, resembling a messaging application. The user experience encourages back-and-forth dialogue and exploration of ideas. It maintains a clear history of conversations, which is crucial for long, multi-turn tasks.
Perplexity’s interface is more akin to a search engine with a chat overlay. When you submit a query, the answer is presented directly, often with immediate follow-up questions suggested by the AI. The source citations are immediately visible, integrated into the response.
The focus is on the efficiency of information delivery. Perplexity also features “Spaces,” which are tools for organizing research around specific topics. The user experience is optimized for speed and verifiable results.
In terms of features, ChatGPT is a true multi-modal platform. The Plus version allows for easy image generation, voice conversations, and direct file uploads for analysis. Perplexity also supports file uploads and text/image input.
However, ChatGPT’s ecosystem, particularly with its Custom GPTs, offers a more versatile platform for building specialized AI tools. Perplexity offers a cleaner, less cluttered interface for those who prioritize fast, cited answers.
What is ChatGPT and How Did it Achieve Such Widespread Recognition?
ChatGPT is a large language model chatbot developed by OpenAI. It was launched in November 2022 and quickly achieved massive global popularity. The “GPT” stands for Generative Pre-trained Transformer. This refers to the core technology that enables it to understand and generate human-like text.
ChatGPT’s recognition stems from its unprecedented fluency and versatility. It could write code, compose music, draft business plans, and engage in complex, coherent conversations.
It showed the public the true potential of advanced generative AI in a highly accessible format. The free availability of a powerful, state-of-the-art model led to rapid adoption.
The model is trained on an enormous dataset of text and code from the internet. This training gives it a deep understanding of language patterns, facts, and reasoning. While its initial free version was limited to the knowledge in its training data, its conversational skill was unmatched.
It has continued to evolve with models like GPT-4 and GPT-5, adding real-time web access, image generation, and other advanced features. It became the benchmark against which all subsequent conversational AI tools are measured.
What are the Main Factors That Make Perplexity a Stronger Choice for Researchers?
Perplexity AI is a superior choice for researchers due to its architectural emphasis on factual verification and currency. It is designed to be an “Answer Engine” rather than a general-purpose conversationalist. The mandatory inclusion of real-time web sources is the primary factor.
This feature ensures the information is up-to-date, unlike a model relying solely on static training data.
Perplexity’s Pro Search feature digs deeper into diverse sources, including academic and industry reports. It provides a rigorous level of information synthesis. Researchers can quickly gather comprehensive overviews on complex or niche topics. The transparency of citations allows researchers to easily follow the trail of information.
The ability to use the “Search Focus” feature helps refine the query to specific domains. Users can target their search to Academic papers, Reddit discussions, or YouTube videos. This contextual filtering leads to highly relevant and focused results.
For anyone whose work relies on accurate, citable, and current facts, Perplexity provides an indispensable layer of rigour and transparency. It acts as a dynamic, intelligent layer on top of the entire internet.
A Simple Guide to Choosing Between Perplexity and ChatGPT
The choice between Perplexity and ChatGPT depends on the user’s main goal. If the task is about creation, conversation, or complex coding, ChatGPT is the best option. It is the ultimate tool for generating long-form content, brainstorming ideas, and engaging in deep dialogue. Its Custom GPTs and coding tools offer superior versatility for many professional roles.
If the task is about research, fact-checking, or current events, Perplexity is the better choice. It is the dedicated AI answer engine that prioritizes accuracy and verifiable sources. Its real-time search capabilities and mandatory citations make it the most transparent tool available. For academic work, journalism, or market research, Perplexity provides the necessary rigor.
Ultimately, these two tools are highly complementary. A power user will likely subscribe to both Perplexity Pro and ChatGPT Plus. They will leverage Perplexity for gathering the raw, verified, and cited information. Then they will use ChatGPT to transform that information into polished, customized, or actionable content. Together, they form a powerful, dual-purpose AI workflow.
The user should start with the free version of each tool. This will allow them to test which interface and output style best suits their individual needs. The decision should be driven by the user’s most frequent and critical tasks.
What is a Large Language Model and How Does it Work?
A Large Language Model is a type of Artificial Intelligence algorithm. It uses deep learning techniques to process and generate human language. LLMs are trained on massive amounts of text data from the internet. This training allows them to understand grammar, facts, context, and relationships between words. They work by predicting the next most statistically probable word in a sequence.
How Does an LLM Generate Human-like Text?
LLMs generate text by taking a user’s prompt as input. They then process this prompt through billions of learned parameters. The model calculates the probability of various words that could follow. It selects a word and repeats the process until it forms a coherent and contextually relevant sentence. This sequential prediction creates fluid, human-like dialogue and creative content.
What is the Primary Difference Between a Search Engine and an LLM?
A traditional search engine indexes the web and returns a list of relevant links or web pages. It is an information retrieval tool. An LLM is a generative tool that creates new content. It synthesizes information and generates a direct answer rather than a list of links. Some modern tools like Perplexity combine both functions.
How Do LLMs Handle Information That is Newer Than Their Training Data?
Initially, LLMs could not access new information and were limited by their training cutoff date. Modern LLMs, like the paid versions of ChatGPT and Perplexity, now have web-browsing capabilities. They can search the live internet for real-time, up-to-date information. This allows them to answer questions about current events or new developments.
What is an AI Hallucination and Why Does it Happen?
An AI hallucination is when an LLM generates a response that sounds factual and confident but is entirely false. This happens because the model prioritizes generating fluent, coherent text over factual accuracy. It is essentially guessing a highly probable but incorrect answer based on patterns it learned. Using tools with source citations helps to prevent accepting hallucinated information.
Can LLMs be Used for Coding and Software Development?
Yes, LLMs are extensively used for coding. They can generate code snippets in various programming languages. They help developers debug existing code, explain complex concepts, and refactor code for better efficiency. Some advanced versions can even run and test code in a secure environment.
Are LLMs Subject to Bias in Their Responses?
Yes, LLMs can exhibit bias. The models are trained on real-world data from the internet. If the training data contains societal, cultural, or historical biases, the model may inadvertently learn and reproduce them. Developers are continuously working to filter and mitigate these biases during training.
How Do Free LLM Versions Compare to Paid Subscriptions?
Free LLM versions typically offer access to less powerful or older models. They often have limitations on usage, speed, and access to premium features like web browsing or advanced data analysis. Paid subscriptions, like ChatGPT Plus or Perplexity Pro, provide faster responses, access to the newest and most powerful models, and essential features like source-cited web access.
What are the Security and Privacy Concerns with Using LLMs?
A primary concern is data privacy. Many free LLMs use user inputs to further train and improve their models. Users should avoid entering highly sensitive or confidential information into public LLM interfaces. Paid enterprise plans usually offer better privacy guarantees, ensuring user data is not used for model training.
Is One LLM Likely to Dominate the Market Permanently?
The LLM market is highly competitive and rapidly evolving. No single model is likely to dominate permanently. Companies like OpenAI, Google, Anthropic, and Perplexity are continually releasing new, more powerful models and features. Users will likely continue to choose tools based on their specific needs for research, creativity, or coding.
What are the Main Factors That Make Perplexity a Stronger Choice for Researchers?
The commitment to source transparency is fundamental for the research community. Perplexity’s design inherently supports the academic requirement of verifiability. The user does not have to spend extra time prompting the AI to find sources. The sources are presented alongside the answer by default.
This feature saves a significant amount of time for a researcher. Instead of manually searching for the origin of a claim, the link is right there. The synthesis provided by the AI is also very efficient. It takes information from several articles and consolidates it into a single, cohesive answer. This is much faster than reading through multiple articles oneself.
For technical fields, Perplexity’s ability to pull from niche web pages is a major asset. If a user specifies a focus on “Academic” or “Code,” the model prioritizes those types of sources. This targeted approach leads to more expert-level and relevant information retrieval. It minimizes the noise often associated with general web searches.
Furthermore, the Perplexity Copilot is a helpful research assistant. It engages with the user to refine the question and perform a more sophisticated search. This guided process ensures the user is asking the most effective query. The result is a more accurate, detailed, and trustworthy information set.
How Will the Competition Between Perplexity and ChatGPT Evolve Over Time?
The competition between these two leaders will drive innovation across the entire AI sector. The key areas of future evolution will likely include deeper integration of multi-modal capabilities. Both companies will continue to enhance their ability to handle images, audio, and video inputs. They will also work to improve their core weaknesses.
ChatGPT will focus on improving the rigor and transparency of its sourcing. It will aim to integrate real-time citations as a default feature. This move would directly challenge Perplexity’s primary advantage in the research domain. OpenAI will also continue to push the boundaries of reasoning and creativity with new GPT models.
Perplexity will focus on enhancing its creative and conversational capabilities. As it integrates more powerful, multi-modal models from various partners, its output will become smoother. It may introduce more advanced tools for long-form content generation and complex problem-solving. The goal will be to become a true all-in-one platform for both research and creation.
Another area of competition will be in the enterprise and team-based tools. ChatGPT has a strong lead with its Custom GPTs for specialized, repeatable tasks. Perplexity will need to develop its “Spaces” feature into a more robust, collaborative platform. This would allow teams to organize, share, and collaborate on verifiable research more effectively.
The ultimate winner may be the user. As each platform pushes the other to improve its feature set, users benefit from a more capable and versatile toolset. The ideal scenario is a future where both tools are equally strong in all aspects, forcing the user to choose based on interface preference. For now, the distinction remains clear: Perplexity for verifiable facts, ChatGPT for creation and conversation.
Perplexity vs ChatGPT: The Economics and Accessibility of Both Platforms
The pricing for both platforms is nearly identical for the premium consumer tier. Both Perplexity Pro and ChatGPT Plus cost approximately $20 per month. This similar pricing sets up a direct value-for-money comparison based on features. Users must decide if they value the multi-model research of Perplexity Pro more than the creative and code tools of ChatGPT Plus.
The accessibility of the free versions also plays a significant role in adoption. Both companies offer a free tier that allows users to experience the basic functionality. The free version of ChatGPT, typically running on a smaller, faster model, is an excellent entry point. It allows for basic conversations and content generation, albeit with knowledge limitations.
The free version of Perplexity allows for unlimited “Quick Searches.” These searches provide fast, cited answers using the default proprietary model. It also grants a limited number of “Pro Searches” per day, giving free users a taste of the advanced features. This generosity in the free tier helps researchers who only need occasional deep search capacity.
For the vast majority of users, the free version is sufficient for simple, everyday queries. The paid subscription is an investment for professionals, researchers, or power users. It is an investment in speed, deeper reasoning, and specialized tools. The competitive pricing ensures that these powerful AI tools remain accessible to a wide audience. This accessibility is key to the ongoing revolution in information access and content creation.
The Role of Perplexity and ChatGPT in the Future of Search and Information
The rise of Perplexity and ChatGPT signals a fundamental shift in how people search for information. Traditional search engines will need to adapt their models to survive. Users are increasingly preferring a direct, synthesized answer over a list of ten links. This preference for an “answer engine” over a “link engine” is changing the market.
Perplexity is leading this change by making the synthesis of cited information its core product. It proves that a tool can be both an LLM and a powerful search layer. It sets a new standard for informational transparency online. Its success forces every competitor to adopt mandatory citation.
ChatGPT contributes by showing the value of a conversational interface for search. It proves that the information retrieval process can be a dynamic, multi-turn dialogue. Its ability to take a general query and refine it through conversation is a massive leap forward. It blends the act of searching with the act of problem-solving.
The future of information will be a combination of both approaches. It will be conversational, real-time, highly accurate, and fully cited. The next generation of search will not just give an answer but will also tell you exactly where it found that answer. This evolution will increase public confidence in the information provided by AI tools. The combined strengths of Perplexity and ChatGPT are paving the way for this new digital landscape. This new landscape will be defined by intelligent, verifiable, and personalized information delivery.
Perplexity and ChatGPT’s Impact on Academic Integrity and Learning
The introduction of powerful LLMs has created a new set of challenges for education. Students can now generate papers, reports, and code instantly. This capability forces educators to rethink assignments and assessment methods. The focus is shifting from simply producing content to critical thinking and verification.
Perplexity AI offers a constructive solution to this academic challenge. By emphasizing sources and citations, it teaches students the importance of verification. A student using Perplexity cannot simply copy an answer without seeing the sources. This encourages a deeper engagement with the original material. It transforms the tool from a cheating aid into a powerful research assistant.
ChatGPT’s impact is more centered on accelerating learning and understanding complex topics. It can act as a tireless, personalized tutor, explaining concepts repeatedly or in different ways. It helps students grasp difficult subjects by generating analogies or step-by-step breakdowns. The challenge lies in ensuring students use it for understanding, not just for output.
The educational future involves teaching “AI literacy.” Students need to learn how to prompt these tools effectively and, most importantly, how to fact-check them. The skills of synthesizing information from multiple sources, which Perplexity excels at, will become essential. Learning how to use these LLMs responsibly is now a core part of digital education.
Comparing the Underlying Technology and Model Architectures
While both Perplexity and ChatGPT rely on Large Language Models, their underlying architectures differ in application. ChatGPT is primarily built upon the Transformer architecture developed by Google, specifically OpenAI’s custom implementations like GPT-4o. This architecture excels at sequential data processing and is the engine behind its conversational and creative prowess. The model is optimized for deep context understanding and generating long, coherent sequences of text.
Perplexity leverages a multi-model architecture. Its proprietary Sonar models are often based on open-source frameworks like LLM. However, its strength is its integration of multiple high-performance models (GPT, Claude, Gemini).
This integration uses a sophisticated routing layer that dynamically selects the best model for a specific query. For a coding question, it might choose an OpenAI model. For a complex research question, it might choose a model from Anthropic, then blend the results. This flexibility in model choice, often called an ensemble approach, is a key technical differentiator.
The difference extends to how they handle real-time data. Perplexity’s system is integrated more deeply with the search layer. Its models are instructed to search the web as a primary source, not just a fallback.
This search-first integration is what allows for the nearly instant citation generation. ChatGPT’s web browsing is more of an added tool, where the core model decides when to use the search function. The difference is fundamental: one is a generative engine that can search, the other is a search engine that can generate.
Which AI is Better for Academic Research and Finding Cited Sources?
Perplexity AI is significantly better for academic research and finding cited sources. It is designed as an answer engine that automatically searches the real-time web. It provides clear, clickable source citations with every response, which is essential for verifiable work.
Is Perplexity AI just using ChatGPT's technology?
No, Perplexity AI is an independent company with its own core models, like Sonar. However, its premium plan, Perplexity Pro, integrates and allows users to choose advanced models from other providers, including OpenAI's GPT models (which power ChatGPT).
Which tool is more reliable for real-time, up-to-date information?
Perplexity AI is more reliable for real-time, up-to-date information. Its default mode of operation is to search the live web for every query. ChatGPT's base model relies on pre-trained data with a knowledge cutoff date, though its paid version can search the web.
Which platform should I use for creative writing, storytelling, and content generation?
ChatGPT is the superior platform for creative writing, storytelling, and long-form content generation. It excels at conversational flow, maintaining style, and producing narrative, human-like text due to its core focus on being a conversational AI.
Which AI is better for coding assistance and debugging?
ChatGPT is generally considered better for complex coding and debugging tasks. Its advanced models were trained heavily on code, and the premium version includes a Code Interpreter feature that can run and test code within the platform.
Can I use both Perplexity and ChatGPT together in my workflow?
Yes, using both tools together is the recommended strategy for maximum productivity. Use Perplexity for the research phase—gathering facts, statistics, and verifiable sources. Then, use ChatGPT to write, refine, and transform that accurate information into final content.
Does Perplexity AI offer a free version?
Yes, Perplexity AI offers a free version with unlimited Quick Searches that use real-time web access and citations. It also typically provides a limited number of advanced Pro Searches per day.
What is the main difference between Perplexity Pro and ChatGPT Plus?
Both premium subscriptions cost around $20 per month but offer different strengths. ChatGPT Plus focuses on powerful creative tools, advanced reasoning, and custom GPTs. Perplexity Pro focuses on deeper, multi-model research and more advanced sourcing capabilities.
Why does Perplexity tend to feel faster for quick facts than ChatGPT?
Perplexity is often faster for quick factual answers because its core design prioritizes rapid information retrieval and concise synthesis. It is built to function like a supercharged, answer-first search engine, providing its results directly with sources.
Does the ChatGPT free version have a knowledge cutoff date?
Yes, the base model of the free ChatGPT version typically has a knowledge cutoff date, meaning it cannot answer questions about events or information that occurred after its training was finalized. The paid version mitigates this by adding web browsing capabilities.
