Which One Should You Use? ChatGPT vs Gemini vs Microsoft Copilot
No‑Hype Guide To What They Are Good For
All three are the same species - next-token predictors with the same chronic issues (hallucinations, over-agreement (sycophancy), non-determinism). The only useful differences are where they live and what they can see. As stand-alone chatbots, LLMs are not a defensible product. Quality now comes from integration, not incantation: real-time web search for fresh facts, internal data connections for context, and ecosystem support (files, identity, permissions, workflows).
That’s why the competitive game is about platforms, not prompts or models—and why OpenAI is racing to bundle the model with an e-commerce layer, a device, a social/identity graph, and enterprise suites. Selling “just a chatbot” is how you lose.
This article explains the three platforms you see in real life: ChatGPT, Google Gemini, and Microsoft Copilot and what each is good at. It’s for beginners who feel lost in chatbot hype and want a practical map.
The high level comparison
ChatGPT leads on raw model capability (text/code, voice, image, robust file tools) and is great for quick standalone work. Privacy and data protection in the public app are questionable and they will most likely use your input for training their models. The enterprise version is different and if your company offers one - you are lucky, you have access to quality models with data protection.
Gemini is great at multimodality and vision and is natively embedded in Google Workspace (Gmail/Docs/Sheets/Meet) with Drive grounding. It is the easiest if your organisation lives in Google.
Later in the article: deep dive into the difference between the platforms.
What you’ll learn in 7 minutes:
Which tool to use when (based on where your files and workflows live).
What each tool is great at and its common failure modes.
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