Welcome Deep Research: The World's Best Multi-Model Research System

Most research systems rely on a single AI model. They're fast, but they miss nuance. They're comprehensive, but they lack depth. Deep Research takes a fundamentally different approach: we orchestrate three world-class AI models working in parallel, each bringing unique strengths to create research reports that are both comprehensive and deeply insightful.

The magic isn't in any single model—it's in how we combine them. Perplexity Deep Research brings real-time web citations and current information. Perplexity Reasoning Pro adds analytical depth and stepwise reasoning. OpenAI Deep Research provides comprehensive coverage and synthesis. Together, they create research reports that no single model could produce alone.

But orchestration alone isn't enough. We needed a synthesis model powerful enough to weave these three perspectives into a coherent, Wikipedia-style report. That's where K2-Thinking comes in—the world's strongest reasoning open-source model, capable of understanding complex relationships, identifying patterns across multiple sources, and creating narratives that are both accurate and engaging.

Deep Research doesn't just use multiple models—it uses three carefully selected specialists, each optimized for a specific aspect of research.

Perplexity Deep Research (`perplexity/sonar-deep-research`) is our web citation specialist. It excels at finding authoritative sources, extracting relevant quotes, and providing real-time information from the web. When you need current data, recent studies, or up-to-date statistics, Perplexity Deep Research delivers with numbered citations `[1]`, `[2]`, `[3]` that link directly to sources.

Perplexity Reasoning Pro (`perplexity/sonar-reasoning-pro`) brings analytical depth. It doesn't just find information—it reasons through it. It identifies patterns, draws connections, and provides stepwise analysis that helps readers understand not just what the research says, but why it matters. This model excels at breaking down complex topics into understandable components.

OpenAI Deep Research (`openai/o4-mini-deep-research`) provides comprehensive coverage. It ensures no angle is missed, no perspective is overlooked. This model is particularly strong at synthesizing diverse viewpoints and creating balanced, well-rounded reports that consider multiple sides of complex issues.

All three models work simultaneously—not sequentially. This parallel architecture means we get comprehensive results faster than sequential approaches, while ensuring each model's unique strengths contribute to the final report.

After three world-class models have done their research, we need something equally powerful to synthesize their findings. That's where K2-Thinking comes in—the world's strongest reasoning open-source model, running on Groq's ultra-fast inference infrastructure.

K2-Thinking (based on Kimi-K2) isn't just a language model—it's a reasoning engine. With 251K context window and advanced reasoning capabilities, it can process the combined outputs of all three research models, identify patterns across thousands of words, and create coherent narratives that weave together insights from multiple sources.

Why K2-Thinking? Because synthesis isn't just about combining text—it's about understanding relationships. When Perplexity Deep Research cites a recent study, Perplexity Reasoning Pro analyzes its methodology, and OpenAI Deep Research provides historical context, K2-Thinking sees how these pieces fit together. It identifies contradictions, highlights agreements, and creates a narrative that's greater than the sum of its parts.

The model's reasoning capabilities are particularly crucial for creating Wikipedia-style reports. These aren't just summaries—they're structured documents with proper headings, inline citations, and a references section. K2-Thinking understands document structure, citation formatting, and how to create reports that are both comprehensive and readable.

Running on Groq means K2-Thinking synthesizes reports at blazing speeds—often completing 3000-5000 word reports in minutes, not hours. This speed doesn't come at the cost of quality; K2-Thinking's reasoning capabilities ensure every synthesis is thoughtful, well-structured, and properly cited.

Deep Research follows a carefully designed workflow that ensures comprehensive coverage while maintaining efficiency. It starts with an interactive Q&A stage where users answer targeted questions to refine the research scope. This isn't just a formality—it helps the system understand exactly what you need, ensuring the final report addresses your specific questions.

Once the scope is defined, all three research models begin working in parallel. Each model receives the same research plan and questions, but they approach them differently based on their specializations. Perplexity Deep Research focuses on finding authoritative sources, Perplexity Reasoning Pro emphasizes analytical depth, and OpenAI Deep Research ensures comprehensive coverage.

As the models complete their research, their outputs are collected and prepared for synthesis. This includes extracting sources, formatting citations, and organizing findings. The system automatically aggregates sources from all three models, deduplicates them, and creates a unified source catalog.

Then K2-Thinking takes over. It receives all three research outputs, the source catalog, and the original research plan. Its job is to create a comprehensive, Wikipedia-style report that synthesizes all findings into a coherent narrative. The report includes proper headings, inline citations, and a complete references section—all formatted professionally.

The final report is presented in a beautiful, unified layout inspired by Wikipedia. The main content area displays the synthesized report, while a sidebar shows research details, model results, and quick actions. Citations are clickable, linking directly to sources with hover previews. Users can ask questions via integrated chat, create multiple chat sessions, and export the report as PDF.

We're currently working on Deep Research Ultra—an enhanced version that pushes the boundaries of autonomous research. In our testing, we've seen Deep Research Ultra models work autonomously for 3-4 hours, conducting deep, multi-stage research that goes far beyond what's possible with standard Deep Research.

Deep Research Ultra extends the standard workflow with additional research stages. After the initial three-model parallel research, Ultra can trigger follow-up research cycles based on gaps it identifies. It can dive deeper into specific topics, explore tangential areas, and conduct multi-hour research sessions that produce exceptionally comprehensive reports.

The key to Ultra's autonomy is advanced planning and gap analysis. The system continuously evaluates what it knows, identifies what's missing, and autonomously decides to conduct additional research. This isn't random exploration—it's strategic, goal-oriented research that builds on previous findings.

During our testing, we've seen Ultra produce research reports exceeding 10,000 words, with hundreds of sources, covering topics from multiple angles over several hours. The system maintains coherence throughout, ensuring that even after hours of autonomous research, the final report is well-structured and properly cited.

Deep Research Ultra represents the future of autonomous research—systems that can work independently for extended periods, conducting deep investigations that would be impractical for humans or shorter-running systems. We're excited to bring this capability to users soon.