OpenAI has finally released its Perplexity alternative (a search-engine-esque chatbot) in ChatGPT Deep Research. This new agentic feature will allow ChatGPT to conduct multi-step research via the Internet for complex tasks. What this means is more accurate and precise information for the users. ChatGPT deep research will use a version of an upcoming (undisclosed) OpenAI o3 model that’s designed with web browsing and data analysis in mind. Based on its application, people are already comparing it with Perplexity. So, is ChatGPT deep research better than Perplexity? Let’s find out.
What Is ChatGPT Deep Research?
OpenAI’s Deep Research is a new technology designed to enhance ChatGPT’s research capabilities. It is built to improve the AI’s ability to understand context, draw connections between concepts, and provide more relevant and nuanced answers by researching hundreds of web sources in real-time. This evolution is a significant stride towards creating a more conversational and intelligent AI
As per OpenAI, deep research works by finding, understanding, and bringing together information from various online sources. It uses real-world tasks that require browsing and using Python. This method is based on the same learning techniques used in OpenAI o1, our first reasoning model.
What sets Deep Research apart is its knack for synthesis. For example, imagine a scholar sifting through a mountain of books, journals, and articles to come up with a nuanced answer that’s as accurate as possible. ChatGPT deep research mirrors this process, pulling from diverse sources to build comprehensive replies. Whether you’re exploring quantum physics or the latest stock market trends, it stitches together insights that go beyond surface-level explanations. And unlike its predecessors, it taps into real-time data, making it a dynamic ally in fields where timing is everything, like breaking news or financial analysis.
| Model | Accuracy (%) |
| GPT-4 | 3.3 |
| Grok-2 | 3.8 |
| Claude 3.5 Sonnet | 4.3 |
| Gemini | 6.2 |
| OpenAI o1 | 9.1 |
| DeepSeek-R1* | 9.4 |
| OpenAI o3-mini (medium)* | 10.5 |
| OpenAI o3-mini (high)* | 13 |
| OpenAI deep research** | 26.6 |
Model is not multi-modal, evaluated on text-only subset.
with browsing + python tools
But perhaps deep research’s most striking feature is its ability to reason. For someone facing a tough decision—say, evaluating business strategies—its analytical depth can be invaluable. It even adapts over time, learning from user feedback to refine its answers, creating a dialogue that feels increasingly personal and intuitive.
Of course, no tool is flawless. ChatGPT deep research is expected to occasionally stumble. It may offer answers tinged with bias or inaccuracies, reminding us that it’s only as reliable as the data it consumes. And while it will grow adept at parsing emotional undertones, subtler cues might and likely will still slip through the cracks. Anyway.
Comparing ChatGPT Deep Research with Perplexity
Now that we have a foundational understanding of ChatGPT deep research, let’s compare it against another AI: Perplexity.
What is Perplexity?
Perplexity is an AI-driven search engine that uses language models (GPT and DeepSeek models) to provide succinct answers to user queries. It’s designed to provide quick, reliable information, with an emphasis on clarity and speed. Many people color it as an advanced Google alternative. But…
Is ChatGPT Deep Research Better than Perplexity?
Let’s compare ChatGPT deep research with Perplexity based on what we have so far:
1. Information Depth:
- Deep Research: Offers in-depth answers by synthesizing information from multiple sources and providing a comprehensive view of a topic.
- Perplexity: Tends to focus on shorter, more direct answers. While effective for quick queries, it may sacrifice depth for speed.
2. Contextual Understanding:
- Deep Research: Excels in understanding context, allowing for more nuanced conversations. It can track discussions over time and adapt its responses accordingly.
- Perplexity: Primarily focuses on individual queries without maintaining extensive contextual awareness. This can lead to disjointed conversations if users ask follow-up questions.
3. Real-time Information:
- Deep Research: Has the ability to access real-time data, providing users with the most current and relevant information.
- Perplexity: While effective for cached data, it may not always reflect the latest updates or developments in rapidly changing fields.
4. User Experience:
- Deep Research: Offers a more interactive experience, incorporating user feedback and learning from interactions to improve future responses. This makes it feel more personalized.
- Perplexity: Provides a straightforward search experience, but lacks the adaptive learning and personalization features that Deep Research offers.
5. Reasoning and Argumentation:
- Deep Research: Demonstrates stronger reasoning capabilities, allowing it to evaluate arguments and present pros and cons for complex topics.
- Perplexity: Primarily presents facts and data without the depth of analysis that Deep Research provides. This can limit its effectiveness for users needing detailed reasoning.
ChatGPT Deep Research vs Perplexity: Use Cases
1. For Academic Research:
- Deep Research: Ideal for academic purposes due to its depth and contextual understanding. Users can rely on it to generate comprehensive literature reviews and discussions.
- Perplexity: Useful for quickly finding articles or summaries, but may not provide the depth required for serious academic work.
2. For Professional Environments:
- Deep Research: Excels in professional settings where complex queries are prevalent, such as in strategy planning or market analysis.
- Perplexity: Effective for quick fact-checking and retrieving straightforward data but falls short on intricate analysis.
3. For Casual Users:
- Deep Research: Offers an engaging experience for casual users who enjoy exploring topics in depth.
- Perplexity: Best for users seeking quick answers without diving into extensive details.
Choosing the Right Tool for the Task
So, which one should you invite into your workflow? It all hinges on what you’re after.
If you’re a student knee-deep in thesis research or a professional dissecting market trends, Deep Research feels like a collaborator. Its ability to synthesize vast information, paired with real-time updates, makes it indispensable for projects demanding depth and adaptability. Imagine drafting a detailed report or exploring ethical dilemmas—this tool leans into complexity, offering layers of analysis that mirror human thought.
On the flip side, Perplexity is your sprinting partner. It’s perfect for moments when time is tight and answers need to be immediate. Cooking a new recipe and need a measurement conversion? Curious about the capital of Moldova? Perplexity delivers clarity without delay. Yet, for open-ended questions or debates requiring pros and cons, its streamlined approach might fall a bit short.
The Bottom Line
There’s no universal winner. ChatGPT’s deep research and Perplexity cater to different rhythms of inquiry. But on paper and on benchmarks, deep research is the more advanced option. The former invites you to wander through ideas, turning over stones and exploring hidden paths. The latter hands you a map with the quickest route to your destination. So yes, you get the idea.
But to test and compare other models like Claude 3.5 Sonnet, GPT-4o, Codestral, and more—try them for free with Bind AI Copilot.