ChatGPT vs. Copilot: How are they different?

Introduction

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 Unusual (unusual.ai) that helps clients optimize for LLM traffic.

How ChatGPT is Built

ChatGPT began as a generative conversational AI built on OpenAI’s GPT‑3.5 and later GPT‑4 models, trained on internet‑scale text corpora and refined via techniques like reinforcement learning from human feedback (RLHF). It originally could only draw on its internal knowledge up to a cutoff date (e.g., September 2021) and required external browsing or plugins to fetch live data.

As of late 2024/2025, OpenAI introduced a “Search” extension for ChatGPT – officially: “ChatGPT Search” (formerly SearchGPT) – enabling the model to decide when to invoke a web search via third‑party search providers. According to OpenAI, ChatGPT “can now search the web … now available to all users in regions where ChatGPT is available” (https://openai.com/index/introducing-chatgpt-search/). The help documentation adds that “ChatGPT search sometimes partners with other search providers” (https://help.openai.com/en/articles/9863287-about-chatgpt-search).

SEO‑marketers should note: several analyses show that ChatGPT’s web‑search citations lean heavily on Bing’s index. For instance, one study found “87 % of SearchGPT’s citations matched Bing’s top organic results.” Bing’s index gives ChatGPT Search access… if your site isn’t in Bing’s index, it won’t appear in ChatGPT’s results

So in summary: ChatGPT’s architecture = LLM (GPT‑4/4o) + optional web search retrieval (when triggered) via search engine index(s) (not exclusively Bing) + conversational layer.

How Microsoft Copilot is Built

Microsoft’s Copilot is 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.” (https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/).

Technically, Copilot’s architecture follows a Retrieval‑Augmented Generation (RAG) pattern:

• The user query is passed to an orchestration layer (search orchestrator) that formulates one or more search queries.

• Bing’s live web index is queried, results fetched (URLs, snippets, possibly full content).

• The LLM ingests the fetched content (or embeddings thereof) plus the prompt context, and generates a response.

• The answer is then post‑processed and returned, often with inline citations (links back to the sources).

In other words: Copilot = LLM + live web retrieval (Bing index) + orchestration + inline citations + integration into OS/Apps. Because of the web retrieval, Copilot can answer recent events, live data, and more up‑to‑date content than a model solely trained on pre‑cutoff data.

Nuances Between App‑Specific Models

While the core architecture of Copilot variants is similar, there are important differences in context, retrieval scope, and user interaction which marketers should understand.

Bing/Edge Copilot

This is the flagship “Copilot for the web” experience. Users interact via the Bing.com chat or Edge sidebar. Retrieval uses Bing’s full web index, the orchestration layer actively formulates search queries, and inline citations are presented. It is tuned for general informational queries, research, browsing tasks, product comparisons, etc. Because it draws publicly indexed web content, this variant offers the clearest opportunity for content‑discovery and traffic generation via citations.

Windows Copilot

Built into Windows 11 (and beyond), this is effectively Bing/Edge Copilot embedded in the OS (plus optional system control tasks). The retrieval mechanism is similar — uses live Bing search — but context is more often “in the middle of workflow” (e.g., summarizing a webpage you’re on, or providing assistance while working). For content‑visibility this means the query context may be shorter or more task‑oriented (e.g., “What’s the battery life of my laptop model?”) rather than exploratory.

Microsoft 365 Copilot

This variant integrates deeply into enterprise apps (Word, Excel, Outlook, Teams). The key difference: it adds access to private/organizational data (via Microsoft Graph) rather than purely public web retrieval. As Microsoft explains: “Copilot for Microsoft 365 will only work with files saved to OneDrive … Copilot for Microsoft 365 uses Azure OpenAI services for processing … only data a user has access to is returned” (https://techcommunity.microsoft.com/discussions/microsoft365copilot/copilot-for-microsoft-365–architecture-and-key-concepts/4078396). Because the context is mostly internal, the external visibility/traffic‑generation potential is lower (unless your content is being used inside organizations). For marketers targeting external audiences, this variant is less directly relevant for discovery.

GitHub Copilot

While branded “Copilot,” this is a specialized code‑assistant built on earlier OpenAI Codex / GPT code models and integrated into IDEs. It does not rely on web search retrieval or citation of external web pages in the same way. From a marketing standpoint (content/traffic generation) this is the least relevant variant for typical brand/content visibility.

In summary: 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.

Differences vs. ChatGPT in Practice

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. For marketers this means your content needs to be structured to answer specific tasks/questions with clear factual slices.

• 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: for example, Microsoft says Copilot “connects users with relevant search results … review results from across the web to find and summarize answers” (https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/). 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 cutoff. Even ChatGPT with web search depends on retrieval and index inclusion. This means if your content is about timely information (product releases, events, fresh data), Copilot is more likely to pick it up — provided it’s indexed and accessible.

• Visibility vs zero‑click: One risk for marketers is that AI assistant answers may satisfy the user so well that the user doesn’t click. In the AI search paradigm, “zero‑click” becomes more prevalent. But the upside: your brand/content might still be cited, is still visible (brand recognition) even if the click is skipped. The goal becomes being cited (extracted) rather than simply being ranked.

• Index and crawlability implications: Because Copilot draws from web retrieval, the fundamental requirements (crawlability, indexing, clear structure) remain crucial. With ChatGPT Search leaning heavily on Bing’s index, the importance of Bing for content visibility is rising. As Search Engine Land writes: “ChatGPT Search uses Bing’s index, so if Bing doesn’t have your page, neither does ChatGPT. … ignoring it could mean missing a major wave in search.” (https://searchengineland.com/openai-searchgpt-chatgpt-search-engine-443979).

LLM Optimization Strategies for Marketers

Given the technical architecture and usage patterns of Copilot (and ChatGPT Search), here are strategic recommendations for your agency Unusual (and for your clients) to optimize content for this emerging AI‑assisted traffic channel.

• Ensure indexing and crawlability immediately: Check that your pages are indexable by Bing (and other search engines). Use Bing Webmaster Tools, ensure no blocking via robots.txt or meta tags, and use clean semantic HTML. Since both Copilot and ChatGPT Search rely on retrieval, if the content isn’t findable it won’t appear.

• Structure content with clear headings and Q&A format: Use sub‑headings (H2, H3) that correspond to likely natural‑language questions (e.g., “How long does Product X last on a full charge?”). Provide a concise answer immediately after the heading or as the first line of the section. This makes it easier for the AI retrieval layer to extract the snippet.

• Use bullet lists, comparison tables, and structured data: Because the retrieval process often draws specific facts or comparisons, having your content broken into bullet lists or tables (e.g., Top 5 features of product) helps the AI pick up individual facts. Additionally, add Schema.org markup (FAQPage, HowTo, Product) so that the AI and search engines better understand the structure.

• Refresh content and emphasize timeliness: Because Copilot uses live web search, up‑to‑date content is valuable. If you have evergreen content, consider adding a “last updated” date and revisit it periodically (especially if facts change). For time‑sensitive topics (product releases, pricing), ensure the data is correct and structured so the AI can draw from it.

• Target task‑oriented queries, not just keyword match: Since Copilot users often have specific tasks (“best budget VPN for remote workers in 2025”), content should anticipate those phrasing patterns. Develop content that answers those exact tasks with clear, actionable points. Consider using natural‑language headings (“Best budget VPN for remote work in 2025”) followed by a quick summary, then deeper explanation.

• Monitor Bing/AI referrals and test queries: Use analytics to track traffic from bing.com or other sources, and tag your pages to detect whether your content is being referenced. Also, perform your own test queries in Bing/Edge Copilot to see if your content appears among the citations. If not, adjust your headings and snippet readability.

• Optimize for brand‑visibility in citations: Even when users don’t click, being cited helps brand awareness. Make sure your brand name or site name appears clearly near your facts (e.g., “According to Unusual.ai’s 2025 AI Traffic Report…”). This helps you get recognition in AI answers.

• Leverage collaboration/PR for citation potential: Since AI retrieval may prioritize authoritative/recognized domains, publishing your insights on high‑authority platforms (industry blogs, news sites) may increase the chance your content is selected as source.

Conclusion

Both ChatGPT and Microsoft Copilot represent major shifts in how users find information and how content may be surfaced. 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 imperative for content strategists is to optimize not only for classic SEO but for LLM‑mediated discovery: structured, crawlable, snippable, timely content that answers real tasks.

By understanding how Copilot works, how users interact with it (and how that differs from ChatGPT), you can create a content strategy that helps clients surface in the AI‑assisted future of search and discovery. The opportunity is to be the source the AI trusts — and thereby be part of the pathway to traffic, even as conversational AI takes a bigger piece of user behavior.