OpenAI's Unified Router

A smart router analyzes your query and sends it to the best model for the job—optimizing for speed or power automatically.

Google's Mixture of Experts (MoE)

An enormous model with many specialized "experts." Only the most relevant experts are activated for each task, making it powerful yet efficient.

Perplexity's RAG Hybrid

A Retrieval-Augmented Generation (RAG) system that first scours the live web for information, then uses an LLM to synthesize a cited answer.

Perplexity's Approach: Speed & Structure

Perplexity's "Deep Research" (often called Copilot) is its core product. It's an agentic system that autonomously performs dozens of web searches to synthesize a structured, comprehensive report. It is highly optimized for this specific workflow and is praised for its speed, often completing its analysis in under three minutes. The output is typically a well-organized summary with clear headings and direct citations, designed for quick consumption and verification.

Gemini's Approach: Depth & Narrative

Google's "Deep Research" feature within Gemini often produces the most comprehensive and narratively rich reports. User tests show it can consult hundreds of sources to generate detailed, multi-page documents. This feature is also tightly integrated with NotebookLM, allowing users to easily save, synthesize, and analyze large collections of source documents, making it a powerful tool for in-depth projects that require more than a summary.

Perplexity: The RAG Specialist

Perplexity's accuracy comes from its specialized RAG process: it continuously searches, retrieves, and synthesizes information as its core function. This is validated by its exceptional 93.9% score on the SimpleQA benchmark for factuality. However, its reliance on its own web crawlers, which have faced controversy, presents a potential vulnerability if publishers choose to block them, which could impact the breadth of its data sources.

Gemini: The Search Engine Giant

Gemini's accuracy is backed by the power of Google Search. When grounding responses, it leverages the world's most comprehensive and battle-tested index of the internet. This provides immense scale and a sophisticated, time-tested system for ranking information quality and authority. For users, this means the accuracy of its web browsing is built on a foundation of decades of search engine development.

The "Feels Dumber" Narrative

Despite impressive benchmark scores, GPT-5's launch was met with user criticism, with many claiming it felt like a "downgrade" from GPT-4o. OpenAI's CEO acknowledged a launch-day failure of the automatic model-routing system was partly to blame.

This highlights a growing disconnect between raw intelligence and user experience. For mass adoption, factors like low latency, predictable behavior, and conversational tone are becoming as important as a model's ability to solve complex problems.

Ethics, Data, and Strategic Vulnerabilities

Perplexity faces a significant controversy over its data-gathering practices, with Cloudflare accusing its bots of deceptively scraping content and ignoring `robots.txt` directives. This escalates the tension between AI companies needing data and publishers protecting their IP.

This exposes a unique strategic vulnerability for Perplexity. Its core value—providing real-time, web-grounded answers—is entirely dependent on its ability to crawl the internet. If publishers widely adopt tools to block its crawlers, its business model could be severely threatened.