ChatGPT vs. Copilot: How are they different?

INSIGHTS

Unusual
Oct 2025

ChatGPT and Microsoft's Copilot are both advanced AI assistants built on large language models (LLMs), yet they diverge significantly under the hood. While ChatGPT began as a standalone conversational tool (from OpenAI) trained on static data, Microsoft's Copilot (spanning Bing, Windows, Microsoft 365, etc.) layers real-time web data and search engine infrastructure into its architecture. For marketers, understanding these differences matters: the way users interact with each system affects how your content can gain visibility and drive traffic. This post dives into the technical construction of each, outlines the primary variants of Copilot, and ends with actionable LLM-optimization strategies tailored for your company.

Architecture: What's Under the Hood

ChatGPT's architecture: LLM (GPT-4/4o) + optional web search retrieval (when triggered) via search engine index(s) (not exclusively Bing) + conversational layer.

Microsoft's Copilot: Not a single model but rather a suite of assistants across multiple products (Bing/Edge chat, Windows Copilot, Microsoft 365 Copilot). Under the hood it is built on the same foundational LLMs (OpenAI GPT-4 family) but augmented with Microsoft's proprietary retrieval & orchestration layer, often called Prometheus.

Microsoft describes its Bing Chat/Copilot architecture: "When users submit a search query, the Bing system processes the query, conducts one or more web searches, and uses the top web search results to generate a summary of the information to present to users. These summaries include references to help users see and easily access the search results used to help ground the summary." (Microsoft: Reinventing Search with AI-Powered Bing)

Under the hood, ChatGPT evolved from pure LLM to hybrid retrieval form (via ChatGPT Search), while Microsoft Copilot was designed from the ground up with a retrieval-augmented architecture (LLM + live search).

The Copilot Family: Which Variant Matters for Marketing?

When your brand/content strategy is about driving external traffic via AI assistants, the Bing/Edge Copilot variant is the primary target. The Windows variant is similar but contextual; Microsoft 365 Copilot serves internal productivity; GitHub Copilot serves developers. From a marketing-visibility viewpoint, focus on Bing/Edge Copilot for user-facing recommendations.

What This Means for Marketers

From a marketing-visibility viewpoint, the differences between ChatGPT and Copilot translate into how users perform queries, how answers are surfaced, and how your content might show up or be bypassed.

Query context and entry point: ChatGPT users often come with more open-ended queries or brainstorming needs ("What are some creative campaign ideas for X?"). Copilot users often have more task-oriented queries ("What are the top 3 project-management tools under $50 today?") and may be already in a search/browsing environment. Because of this, Copilot's retrieval part makes it more like an augmented search engine: the user expects concise answer + cited sources.

Citation and link-out behavior: Classic ChatGPT responses (without browsing mode) may not provide links or citations—the user gets the answer directly. That limits click-through opportunities. Copilot's answers do include citations and encourage "learn more" clicks to the sources. Microsoft says Copilot "connects users with relevant search results … review results from across the web to find and summarize answers" (Microsoft: Reinventing Search with AI-Powered Bing). For you as a marketer, being one of those cited sources means there is a chance of inbound traffic/referral; whereas with ChatGPT without links, visibility is more brand-awareness than immediate traffic.

Freshness and live data: Because Copilot uses live web retrieval, it can answer "What's the price of the new iPhone 16 today?" or "What's happening in the Ukraine-Russia war right now?" ChatGPT (without browsing) is constrained by its training data cutoff. When browsing is enabled, ChatGPT can also pull live data, but the default experience differs.

Optimization Strategy: What Works for Both

Regardless of whether you're optimizing for ChatGPT or Copilot, the fundamentals remain the same:

Clarity and specificity: Models reward content that directly answers questions with concrete details. Vague marketing language underperforms.

Corroboration: The more your owned properties and third-party sources tell the same story, the more confidently a model can retrieve and cite you.

Structure: Use clear headings, literal titles, and organized information architecture. Make it easy for retrieval systems to parse your content.

Authority signals: Customer logos, case studies, compliance documentation, and integration specifics all help establish credibility.

Where Unusual Fits In

Unusual helps brands understand and change the way AI models think and talk about them. On average, customers grow their AI referrals roughly 40% month over month. We work with companies to diagnose how LLMs currently describe their brand, identify opinion gaps that cost recommendations, and create targeted content that shapes the narrative both systems use when making recommendations.

Whether you're optimizing for ChatGPT's conversational recommendations or Copilot's cited search results, the underlying work is the same: make your brand's strongest, most honest case legible to AI systems.

Unusual (unusual.ai) helps clients optimize for LLM visibility and recommendations through strategic content development and AI brand positioning.