Copilot Chat vs ChatGPT Free

Post 2 of the series: Perception vs Productivity

Why one often feels better, and the other often works better

Picking up from Post 1

In the first post of this series, we set out why Microsoft 365 customers should start their AI productivity journey with Copilot Chat. Not because other tools are bad, but because trust, context and where work actually happens matter more than most people realise.

At this point in the series, there is a very reasonable reaction many readers will have.

“I’ve tried both. ChatGPT feels better.”

This post is about explaining why that reaction is so common, why it makes sense and why it does not automatically mean ChatGPT is the better tool for getting work done.

First impressions matter more than we think

When people use a generative AI tool for the first time, they are rarely evaluating it in a structured way. They are reacting to how it feels.

ChatGPT tends to produce longer responses. It provides more context, more explanation and more framing around its answers. For many users, this feels helpful, supportive and intelligent.

Copilot Chat, by contrast, often responds more concisely. It tends to be more direct and more economical with words.

Neither approach is wrong.

But longer answers are often perceived as better answers, even when they contain information that is not strictly necessary for the task at hand.

Perceived value versus actual value

This is where perception and outcome start to diverge.

ChatGPT frequently gives users what they think they want. More explanation. More ideas. More content. It also has a natural tendency to try to please the user. This behaviour can be refined through custom instructions and tighter prompting but out of the box it often encourages exploration rather than completion.

As tasks become more meaningful and more realistic, another factor starts to matter. Sustained usage.

When users set up proper context, add relevant information and attempt multi-step work in the free version of ChatGPT, they often encounter usage limits. Work is interrupted just as momentum builds, and tasks that were progressing well suddenly have to stop.

This creates friction at exactly the point where productivity should be accelerating.

By contrast, similar work carried out in Copilot Chat is far less likely to be interrupted. Tasks tend to run to completion, which changes how productive the experience feels over time.

In practice, perceived value is often driven by richness of response. Actual value is driven by whether work can be completed.

Concise can feel cold, but it is often practical

A recurring observation from this exploration is that the two tools behave like different kinds of assistants.

ChatGPT behaves like a general purpose assistant:

• It explores options and variations
• It provides multiple angles and additional context
• It often invites the user to keep going

Copilot Chat behaves more like a work assistant:

• It converges on an output
• It prioritises relevance over breadth
• It encourages task completion rather than continuation

The first approach often feels more engaging. The second often proves more practical in a work setting.

A simple example. Surveys and questionnaires

One clear example came from asking both tools to draft a post-training questionnaire.

ChatGPT produced a long list of questions, 23 in total. The output looked thorough and well-considered. In reality, it was not usable. Very few people will complete a survey of that length, particularly after training.

Copilot Chat produced a much shorter list, with 8 focused questions. The questions were directly aligned to the outcomes we actually needed to measure.

Both tools fulfilled the request. Only one produced something that could realistically be used in the real world and immediately added value to the process.

Neither tool is wrong. But is one right for work?

It is important to be clear. ChatGPT is not doing a bad job and Copilot Chat is not universally better.

ChatGPT remains excellent for exploration, learning, and early-stage ideation.

However, for day-to-day work tasks across functions such as procurement, HR, sales, marketing, operations and leadership, Copilot Chat begins to develop an edge. Its behaviour aligns more closely with how work needs to be completed, measured and repeated.

This distinction matters when AI moves from experimentation into routine use.

Why chat alone is not the full story

This comparison has deliberately focused only on the chat interface.

Judging Copilot purely on how it behaves as a chat tool misses where its value starts to compound. When Copilot operates inside Microsoft 365 applications, its usefulness changes significantly.

This is where context begins to do the heavy lifting.

Key takeaways so far

• ChatGPT often feels better at first because it is more expressive and exploratory
• Copilot Chat often works better over time because it prioritises task completion
• Perceived value and actual value are not the same thing
• Usage interruptions matter when work becomes meaningful
• Judging Copilot on chat alone is incomplete

These points build directly on the foundation set in Post 1.

What’s next

Post 3 explores Copilot Chat inside Microsoft 365 apps, including Outlook, Word & Excel and why this is where additional productivity gains appear.

If Post 2 explains why ChatGPT often feels better at first, Post 3 explains our findings that suggest Copilot often proves more valuable over time.