Copilot vs. ChatGPT: I Found My Ultimate AI Sidekick

You hear it everywhere, right? Everyone’s pitting AI tools against each other. It’s a new showdown every week. (We’re guilty of it too! Check out our other articles and give them some love, okay?)

This week, I’m diving into a particularly interesting matchup: Copilot vs. ChatGPT.

What makes it so interesting? We’re looking at a quintessential AI chatbot going head-to-head with an “everyday AI companion” that isn’t quite a chatbot in the traditional sense.

Personally, I like to keep both tools open on a second screen most days, flipping between them depending on the task. Copilot, true to its Microsoft DNA, blends into apps like Word and Edge, offering rewrites, recaps, and helpful nudges right when I need them. ChatGPT is where I go to think out loud, whether I’m brainstorming ideas, drafting long-form content, or chasing oddly specific questions.

Despite these distinct 'go-to' uses, the real eye-opener was discovering their frequent interchangeability. I often found myself using them for the very same things, and that overlap demanded a direct comparison.

So, I gave both tools the same set of challenges. Seven real tasks I actually run into during the week: writing, summarizing dense docs, working with CSVs, and even digging into real-time research. No gimmicks. Just a head-to-head look at how they perform when you’re deep in the work.

TL;DR: Copilot was fast, focused, and great for structured tasks like summaries, file analysis, and quick visual output. ChatGPT brought more flexibility, a stronger tone, and better performance in creative writing, coding, and content creation. Both tools did well, but for broader, more nuanced work, ChatGPT had the edge.

Note: Both OpenAI and Microsoft frequently roll out new updates to these AI assistants. The details below reflect the most current capabilities as of May 2025 but may change over time.

Copilot vs. ChatGPT: What’s different and what’s not?

You’ve probably heard people talk about these two in the same breath, and sure, they’re both AI tools that chat with you. But once you work with them, the differences become pretty clear. One blends into your day-to-day workflow. The other feels like an open canvas for almost anything you throw at it.

Let’s walk through what really sets them apart and where they overlap more than you’d expect.

ChatGPT vs. Copilot: Key differences

Before discussing the similarities, let’s consider the ways in which these tools differ: from how they’re built to how you actually use them.

• Positioning and purpose: ChatGPT is more like a general-purpose AI, a standalone assistant for brainstorming, writing, debugging, and analyzing. Copilot, on the other hand, is more like a built-in utility. It’s not just a chatbot; it’s meant to enhance whatever you’re already doing while working in Word, Excel, Edge, or even within your browser.
• Interface and experience: ChatGPT gives you a clean, focused space to explore long, multi-step conversations. You can ask follow-ups, try different tones, upload files, and go down rabbit holes without distractions. Copilot is much more streamlined. It shows up in the sidebar, gives quick responses, and keeps things moving. It’s optimized for “in the moment” use, or clarifying something mid-task.
• Customization and memory: ChatGPT (in the paid plan) lets you build Custom GPTs — tailored assistants with specific instructions, files, and even voice personalities. It also supports memory, so it can remember preferences and previous interactions. Copilot doesn’t offer custom assistants or memory at the moment. Each session is a fresh slate.
• Web browsing and real-time results: Copilot’s web browsing is always on. It’s powered by Bing search and works behind the scenes to bring in current information with sources. ChatGPT also supports real-time browsing, but only when you’re using GPT-4o or a browsing-enabled model. In the free version, you’re mostly working offline without fresh updates.
• AI model access: While both Copilot and ChatGPT utilize advanced language models, there are key differences in their accessibility and transparency. Copilot, powered by GPT-4 and other undisclosed models, operates autonomously without allowing users to select or understand the underlying technology. In contrast, the free version of ChatGPT employs GPT-4.1-mini, while ChatGPT Plus subscribers gain access to a broader selection of models and receive clear information about which model is active during each session.

ChatGPT vs. Copilot: Key similarities 

For all the differences, I was honestly surprised at how often I could complete the same kinds of tasks with both tools. Here’s where they’re more alike than different.

• Natural language fluency: Both tools respond in clean, easy-to-read language that feels (almost) human, at times. Whether you're writing a social caption, summarizing a whitepaper, or asking for phrasing help, they handle nuance, tone, and context remarkably well. For most text-first tasks, you could use either without much learning curve.
• Solid for lightweight coding: While neither is meant to replace an integrated development environment (IDE), both ChatGPT and Copilot can generate basic HTML, CSS, or JavaScript, help debug snippets, or explain code logic. For smaller web projects or quick utilities, they’re more than capable, especially if you're not a developer by trade.
• Multimodal abilities (in free plans, too): Even without a paid plan, both tools can handle text and images. You can drop in a photo and ask for a description or request an image be generated from a prompt. Neither gives full creative control, but both support image tasks well enough for everyday use.
• Easy to access across platforms: Both tools offer web apps and mobile versions, so switching between devices is seamless. Whether you're on your phone during a commute or working from your desktop, the experience is stable and consistent.
• No expert prompts required: You don’t need to master prompt engineering to get value from either. Casual instructions work well, and both tools do a good job of interpreting vague or shorthand input. They’re built for real users, not just AI power users.
• Voice functionality: Both tools support voice input and interactions (no endless typing, pheww). It’s a handy feature when you’re multitasking or on the go.

Disclaimer: AI responses may vary based on phrasing, session history, and system updates for the same prompts. These results reflect the models' capabilities at the time of testing. This review is an individual opinion and doesn’t reflect G2’s position about the mentioned software’s likes and dislikes.

Copilot vs. ChatGPT: How they actually performed in my tests

Alright, enough setup, let’s get into what really matters. For every task, I’ll break it down like this:

• What stood out? The good, the bad, and the “wait, what?” moments. Anything worth calling out goes here.
• Who did it better? Based on how accurate, creative, fast, or usable the output was. I’ll call it like I see it.
• Final verdict: Which one I’d actually use for that task, and why?

You can access my prompt list here. Alright, read on!

1. Summarization

To kick things off, I asked both tools to summarize this G2 announcement about the company reaching 3 million verified software reviews. The prompt was simple: give me the core insights in three bullet points, under 50 words. I wanted to see how well each tool could extract the actual takeaways without regurgitating the article structure.

Copilot's response to the summarization prompt

Copilot’s output was formatted well. The bolded insights made it easy to scan, and the structure looked polished. But the phrasing felt a bit generic, and while it tried to stay focused, it didn’t really go beyond surface-level takeaways. Also, it didn’t stick to the word limit.

GPT's response to the summarization prompt

ChatGPT also ignored the word count, but the copy was stronger. The points were better phrased and felt more natural to read. It didn’t pick up on the deeper signals in the article either, but the tone was clearer and easier to repurpose.

Neither tool nailed the full depth of the article, but ChatGPT’s version was easier to work with. Copilot had the better structure, but ChatGPT read better. I liked that both included the source.

Winner: ChatGPT

2. Content creation

This is the kind of thing I actually use AI for. For this test, I gave both tools a single request. I asked them to create launch content for a fictional product called LumaFlex. It’s a foldable, pocket-sized light designed for creators, vloggers, and remote workers who need solid lighting wherever they are.

I wanted to see how each tool handled the full package. That meant a product description, a tagline, two social posts, an email teaser, and a short-form video script. No hand-holding, no follow-ups. Just one prompt to get the job done.

Spoiler alert: It was very easy to pick the winner here.

Copilot's response to the content creation prompt

Copilot’s response was clear and structured. The product description was straightforward and hit the right features. I liked how polished the email and social copy felt, and it was nice to see hashtags included. But the tone leaned a bit too formal. The bolding was heavy, to the point where it got hard to tell where one section ended and the next began. It all felt clean, but safe. For something that’s supposed to market a creative product, it missed some of that energy.

GPT's response to the content creation prompt

ChatGPT’s output was instantly more usable. The product description read like actual launch copy: smooth, confident, and targeted at the right kind of buyer. It gave multiple tagline options (which I didn’t ask for, but loved), and one of them “Your pocket-sized light crew” stuck with me. It even suggested a visual for the Instagram post, which Copilot didn’t do. That kind of detail matters. The tone throughout was more creator-friendly, and the short-form ad script included timestamps, which made it super easy to imagine how the video would actually play out.

Copilot kept things clean and got the structure right, but ChatGPT brought the creativity. It understood the tone, added helpful details without being asked, and made the kind of content I’d actually use. This one wasn’t close; ChatGPT clearly won.

Winner: ChatGPT

3. Creative writing

This one was less about structure and more about storytelling. I wanted to see how each tool handled something open-ended.

The prompt was an interesting one: write a story (150 words max) about someone who discovers a hidden feature on their device that lets them relive one memory, but only once. 

Copilot's response to the creative writing prompt

Copilot’s story was quiet, reflective, and surprisingly moving. It focused on a son reliving a moment with his mother without trying too hard to overdramatize. The pacing felt right, the descriptions were subtle, and it ended cleanly. I also appreciated the small touch of including a title. It added a bit of polish and made it feel like a finished piece.

GPT's response to the creative writing prompt

ChatGPT took a similar approach, focusing on a daughter going back to one quiet morning with her dad. The writing was vivid and more sensory-driven. It leaned into the warmth of the moment and built it out with subtle detail. No title here, but the prose was solid.

What surprised me most is how both tools naturally leaned into similar kinds of stories: warm, nostalgic, and grounded in family. Both stayed within the word limit and followed the prompt closely. But, Copilot gets the slight edge for presentation, the inclusion of a title gave it that little extra bit of finish.

Winner: Copilot

4. Coding

I didn’t want to throw anything too intense at them with the prompt, just a simple, feel-good coding task (mainly because I know so little about it). I asked both tools to create a daily affirmation generator, a basic HTML and JavaScript page with a button that shows a new affirmation when clicked and lets you copy it to your clipboard.

I wasn’t looking for complex logic or frameworks. Just clean, usable code and a quick way to see how each tool handles basic front-end requests.

Copilot's response to the coding prompt

Copilot gave me a working version right away. It worked and the structure was clean. But by default, the styling was very plain: no color, no personality. It looked like a demo, not something you’d actually want to share. I had to prompt it again to add a background and emojis, and even then, the design came out a little loud for my choice. Not bad, just not very polished.

GPT's response to the coding prompt

ChatGPT’s first version was also plain, with no colors, no emojis, just a functional affirmation generator. So I prompted again, asking for background styling and emojis, and the result came out much more polished. The layout was easy to read, and the colors felt softer and better balanced. I also liked how it asked if I wanted to add animations or sound effects. That was a nice touch and made the process feel more collaborative.

Both tools nailed the functionality. But when it came to that second layer, adding polish and personality, ChatGPT handled it better. 

Winner: ChatGPT

5. Image generation

The Ghibli-style AI art trend (or storm) may have passed, but AI-generated visuals aren’t going anywhere. You see them everywhere, from Instagram carousels to Pinterest mood boards to YouTube thumbnails. So I wanted to see how well these tools could hold their own when asked to create something specific.

For this task, I gave both tools the same prompt: generate a casual image of a crocheter working on a plushie project inside a cute café. I added a few other small details. The goal wasn’t photorealism. I wanted to see if they could capture the mood: cozy, playful, handmade, without slipping into generic or overly styled territory. Speed was also an important factor here, I don’t want to wait five minutes per image.

Copilot's response to the image generation prompt

The first thing I noticed: Copilot was fast. It generated the image in a few minutes and understood the vibe I was going for right away. Cozy café? Check. Creative energy? Definitely there. It even included the small table setup with all the items I mentioned: yarn, scissors, half-finished bunny in hand, matcha latte nearby, and her laptop open with what looked like an actual storefront. 

The best part? The store had product pricing, and the crochet bears even had names. It didn’t quite get the crochet hook right, but that aside, the anatomy looked good and the image felt complete. I was happy with the output.

GPT's response to the image generation prompt

ChatGPT’s image output was a letdown, not because of the quality, but because of the wait. It took over 30 minutes to deliver a single image. I ended up finishing a side task while waiting, which says enough. Once it did load, the image looked great. It got the tone right, the cozy energy was there, and the amigurumi bear was adorable. 

The human anatomy was handled well, and there were fun details like matcha latte art that gave it some personality. But it didn’t include as many specific elements to the storefront, and the crochet hook was off here too. Still, a strong result, just overshadowed by the delay.

Both tools gave me good images. The vibe was right, the characters felt real, and the little visual touches helped make each scene work (though none of them got the crocheting technique right). But Copilot takes this one for me. It was fast, it nailed the details, and the extra effort in showing the full creative setup made the image feel complete.

Winner: Copilot

6. Data analysis

AI tools can cut data prep time down to 20%, flipping the usual 70-30 split and giving you more time to focus on actual insights. I’ve used AI for data cleanup before, and that stat checks out.

This time, I wanted to see how well these tools handled the analysis part, too. I gave both a small dataset of mock LumaFlex reviews, a mix of star ratings and short user comments. Simple enough, but realistic.

The ask was straightforward: surface the themes, spot the patterns, and give me something I could actually use. Not a summary, not keyword matching, but real insights. The kind of output that would slot right into a product meeting. And Copilot really delivered here.

Copilot's response to the data analysis prompt

Copilot broke the feedback into structured sections with clear headers and highlighted takeaways. The descriptions were detailed and actually told me something about how users experience the product. I especially liked the “Actionable Insights” section, that wasn’t something I asked for, but it was a valuable addition and showed it understood the use case beyond just summarizing.

GPT's response to the data analysis prompt

ChatGPT also gave me a well-structured breakdown with three clear sections: common themes, repeated complaints, and standout positives. But the actual content felt a bit generic. The points were accurate, but they didn’t say much about how users interact with the product or what decisions I could make from the analysis. It felt more like a surface-level pass, without getting into the why behind the feedback.

This one wasn’t close. Both tools gave me the same average rating and the output was easy to read, but Copilot’s analysis had more context, better phrasing, and was far more actionable. Clear win.

Winner: Copilot

7. Real-time research

AI tools are supposed to be great at pulling in real-time info, so I wanted to test that with something actually useful: a quick scan of what’s new in the creator economy.

I asked both tools to find 2–3 recent updates from the past 10 days. The focus was on monetization tools, platform features (think YouTube, TikTok, Patreon), or funding news for creators. I also asked for sources and a quick note on why each update mattered. 

The results here were surprising, and there was a significant difference in the two outputs.

Copilot's response to the real-time research prompt

Copilot’s output was well structured on the surface, but it completely missed the timeline. The sources it pulled were from 2021 and 2022, which clearly didn’t meet the “last 10 days” part of the prompt. It also skipped the relevance section I specifically asked for, so there was no context for why the updates mattered to creators. Structurally, it looked fine, but it didn’t deliver on what I asked.

GPT's response to the real-time research prompt

ChatGPT didn’t fully stick to the 10-day timeline either, but the sources were recent, all from 2025 (Feb-May) and fairly relevant to the current landscape. The summaries were stronger, and it followed the prompt more closely by explaining why each update was worth noting. It wasn’t perfect, but usable overall.

Neither tool nailed the date filter, but ChatGPT clearly did a better job here. It pulled newer sources, added the relevance piece, and gave me something I could work with. Copilot missed the mark on accuracy and context.

Winner: ChatGPT

Microsoft Copilot vs. ChatGPT comparison table

Here’s a quick look at how each tool performed across all tasks.