Comparison and analysis of OpenAI, Perplexity, and xAI Deep(Re)Search functions
In-depth analysis of the three major research tools in the field of AI to understand how they perform their respective functions in data retrieval, information processing, and real-time response.
Core content:
1. OpenAI Deep Research's reinforcement learning framework and multimodal information processing capabilities
2. Perplexity Deep Research's open source model integration and rapid response features
3. xAI DeepSearch's real-time social integration and intelligent verification functions
1. Core functions and design goals
1. OpenAI Deep Research
• Technical foundation: Reinforcement learning framework based on the o3 model, optimized for web browsing and data analysis, supporting multi-step reasoning and cross-modal information processing (text, images, PDF ).
• Core Competencies:
• In-depth research: Automatically search hundreds of online resources (academic papers, news, web pages, etc.) and generate structured reports (including authoritative citations) covering finance, science, engineering and other fields.
• File support: Users can upload documents (such as data tables, reports) to assist in their research.
• Real-time: Relying on public Internet information, the frequency of knowledge update is synchronized with the network.
• Applicable scenarios: Complex tasks that require several hours of manual research (such as market trend analysis and literature review), which take about 5-30 minutes to generate a 10,000-word report .
2. Perplexity Deep Research
• Technical highlights: Integrates the open source model DeepSeek R1 and multimodal models ( GPT-4o , Claude 3.5 Sonnet , etc.), supports PDF , Markdown export and image generation.
• Core Competencies:
• Quick response: Research tasks are completed in an average of 3 minutes, covering areas such as finance, marketing, and health.
• Multi-database integration: Combined search of academic databases such as Web of Science and Psycinfo can be performed to optimize literature research efficiency.
• Free and open: Free users can make 5 queries per day, Pro users can make 500 queries per day, and the cost is lower than OpenAI .
• Applicable scenarios: tasks with high timeliness (such as real-time event analysis, product research) and lightweight academic retrieval.
3. xAI DeepSearch
• Technical architecture: Based on the Grok-3 model, combined with the real-time data stream of the X platform (formerly Twitter ), it strengthens natural language processing and multimodal analysis.
• Core Competencies:
• Real-time social integration: Capture social media discussions and hot events, and quickly generate concise summaries (such as news event tracking).
• Intelligent Verification: Automatically filter high-credibility information and reduce interference from low-quality content.
• Multimodal support: can process non-text information such as videos and images (such as analyzing NASA black hole simulation videos).
• Applicable scenarios: Quickly verify facts and track public opinion trends. Suitable for journalists, investors and other users who need instant information.
2. Comparison of key dimensions
Dimensions | OpenAI Deep Research | Perplexity Deep Research | xAI DeepSearch |
Breadth of data sources | Full network coverage (including academic, news, and user files) | Academic database + public network | X platform real-time data + traditional web pages |
Output Depth | 10,000-word structured report with rigorous logic | Medium length, focusing on key conclusions | A concise summary that focuses on the key points |
Response speed | Slower ( 5-30 minutes) | Fast ( within 3 minutes) | Extremely fast (response within seconds) |
Multimodal capabilities | Support text, image, PDF | Support text, code, and image generation | Supports text, video, and social media content |
Cost and threshold | Pro users only ( $ 200 / month) | Free, Pro version $ 20 / month | X Premium+ users ( $ 8 / month) |
Typical scenarios | Academic research, strategic planning | Rapid retrieval and cross-domain analysis | Real-time event tracking and social public opinion analysis |
3. Summary of advantages and disadvantages
1. OpenAI Deep Research
• Advantages: Leading in-depth analysis capabilities, rigorous output logic and standardized references, suitable for professional scenarios.
• Disadvantages: slow, expensive, and unable to handle non-research tasks such as code generation.
2. Perplexity Deep Research
• Advantages: High cost-effectiveness, multi-model integration improves flexibility, suitable for daily quick retrieval.
• Disadvantages: Insufficient depth of analysis in professional fields, and academic citation norms are weaker than OpenAI .
3. xAI DeepSearch
• Advantages: Outstanding real-time and social data integration capabilities, suitable for dynamic information needs.
• Disadvantages: Lack of ability to generate long reports, reliance on X ecosystem may lead to data bias.
IV. Comprehensive Recommendations
• OpenAI is preferred if you need professional research reports (such as paper writing, market analysis) and have sufficient budget.
• Perplexity is preferred : if you are looking for fast response and cost-effectiveness (such as daily research and cross-field retrieval).
• Prefer xAI : If you are interested in real-time hot topics and social media trends (such as news tracking and public opinion monitoring).
Combination strategy: deep research ( OpenAI ) + real-time supplementation ( xAI ) + daily retrieval ( Perplexity ) maximizes efficiency.
