Gemini vs. Chatgup’s deep research: for me, the choice is obvious

I’ve been using Gemini’s deep research feature for some time, which has experienced both the original and better version. There is a similar feature in Chattgat, so I decided to give it spin and compare it to Gemini to see which one comes up.

What is deep research?

If you are already aware of a deep research function and how it works, feel free to leave this section. If not, read.

Deep research is primarily your personal AI research assistant, which is capable of producing a comprehensive report on almost any topic in the matter of minutes. The main purpose is to save your time. The same topic will likely require hours to do the same, as you have to find every source yourself.

For example, we say you want to research which laptop is to buy. Your journey usually starts with Google Search, leading to countless best lists, studies and comparison posts. Even after the demand, you may still have questions that do not answer.

Instead, you can use a deep research function. Just provide AI details about the laptop type you are looking for, your budget, preferential brands, and essential features, and AI will compile a detailed report. If you have any additional questions about a particular product or how to compare two products in your shortlist, you can only ask Gemini or Chat GPT directly.

The promise is that you can find all the information you need in the same interface, which saves one ton of time. At least, this is the idea on paper. In my experience, the first version of Gemini was not always good with specific or niche titles, while the better shape is much better. But is this chat better than GPT? Let’s find out.

For my first test, I showed that I was buying a new phone to see which model both chat boats would recommend. I used the same gesture for both chat boats, below.

Gemini provided me with a comprehensive report that was more than 7,500 words long. He cited 55 sources and indicated that he had examined an additional 27 sources that were not directly included in the final report.

I was affected by the report structure. It started with the introduction of the current smartphone landscape and then described my quality in more detail – for example, explaining what the “long software support” and “fast charging” mean in today’s market. He then registered four smartphones that were in accordance with my standards and broke into important parts for me, including charging, pricing, software support and other features. Importantly, it also records both the profession and the adaptation for each device.

The report also includes a table that lists all phones as well, which is compared to the specification. It specifically highlighted areas I focused on my indicators and compared together for each phone about these parameters (such as, four model charging speeds, pricing, and how the software update promises are different).

What I especially praised was that one step in this report went ahead and brought all the models one to four, the first of which is the most suitable according to my standards. And he did a great job – I ranked the handsets in the same order.

However, there was a problem: the phone list was not perfect. These included Pixel 9A, Galaxy S24, Xumi 14, and iPhone 16. This means that he lost Pixel 9, which I have decided. Similarly, he mentioned the Galaxy S25 as “coming” in the introduction, despite the fact that it had already been issued months ago (and thus should have been a basic candidate on S24).

So, when the report structure was excellent, and Gemini really included all the standards I entered, the actual list of the phone – the most important part – was all crossed.

Can Chat GPT do a better job? Yes, in some aspects, but not in others. When it came to the phone list, he also suggested four models, but went with a different choice: Pixel 9, Galaxy S25, Pixel 8A, and iPhone 16. Therefore, it correctly recorded the latest Galaxy S25 instead of the old S24 (unlike Gemini).

As far as the report itself is concerned, it was a very bare bonus compared to Gemini. It was only 1,700 words long and read more like a blog post than a deep research report. He easily entered all phones with a brief detail for each. He did not break things into detailed sections, nor did it include profession and mining analysis for every device.

Although there was a comparison table, it was a basic thing I did not cover all the features I mentioned in my indicator. In this report, one phone was not recommended on others. This included significantly fewer sources – 38 compared to Gemini’s 55 (plus 27 additional checks). It was also slow, it took 17 minutes to make this report, compared to 12 minutes. The Chattagpat had a slight advantage here that his report included an image for each phone, which is an excellent visual touch.

I tested both chat boats with two more reports to see that Chat GPT will perform better. First, I told them to compare detailed comparisons between the Galaxy S25 and Pixel 9 so that I could decide which one to buy. Gemini maintained strangely to think that the Galaxy S25 has yet to be released, which was an important mistake by him. However, the report itself was well -made and quite long. Despite the initial error related to the release of the phone, the ultimate result was amazingly good, as it still recognized all its known species and features for comparison.

Chattgupat this time performed very well, making a good thought and deep report. I probably give him the upper hand here, mainly because he had correctly acknowledged that the S25 had already been released. The final decision and comparison points, however, were very similar to Gemini.

As an interested in finance, I also experienced both with the previous day’s stock market performance report. I asked for statistics about the extent to which major indexes performed, the biggest beneficiaries and losers, analyzing the factors affecting the next trading day, and more.

This was a story here with the initial “best phones” request. For example, the two made some mistakes, such as leaving some companies that were among the most beneficiaries. So, none of them was perfect. However, Gemini was better overall, which provided more depth reports, including tables, which significantly help to digest data faster and more easily.

It is difficult to declare the final winner, because the platform is neither flawless. However, based on my test, Gemini currently holds the edge for deep research works, mainly due to the high quality and depth of its reports. They are usually taller, provide more insights, and information is made with useful elements such as detailed tables and classification.

However, this depth sometimes comes at a price. In my tests, there was a slightly higher risk of factual mistakes than Gemini Chat GPT, such as a permanent mistake on the release status of the Galaxy S25. Chat GPT, making more basic reports, was occasionally more reliable at specific data points. However, he also made mistakes, such as suggesting an old phone model. It is important to note that it was not compatible with every report, so the topic you decide to research depends on that your mileage may vary. When I identified them, both chat boats acknowledged their mistakes, according to their reputation.

Beyond the standard of the report, I prefer Gemini’s research process. Before preparing the full report, Gemini offers a “game plan” or outline, which gives you the opportunity to review and request changes to the scope or attention. This active approach can save time and can lead to a more suitable output. On the other hand, Chat GPT asks to eliminate priorities in relation to clear questions (five or more). These questions do not always feel effective, sometimes re -attack the initial indicators for pre -provided information.

The speed is another area where Gemini has a benefit. In my experience, Chat GPT is 40 % faster than this detailed reports. When it takes more than 10 minutes to generate some reports (Chatgupt took 17 minutes to report the phone), the difference is very noticeable.

Finally, it is very important to approach the output of deep research functions on any chat boot with a degree on any chat boot. Sometimes these reports are impressively accurate and insightful, and at other times, there are plenty of issues you have to question the reliability of the device for important decisions without cross confirmation.

So despite the occasional mistakes, Gemini’s deep research usually offers a more powerful and insightful experience. The depth of analysis, the offer of better data, and the useful “game plan” feature makes it my preferred choice compared to the current offer of chat GPT.

This is not to say that the characteristic of the chatigat is without merit. Reports such as its easy, more blog post can be perfect, or even better for users who do not need to find a more deeper in a topic or who want a quick review.

And as far as the matters and mistakes of these chat boats are going on, I hope that they will be ironed with future refreshments, which will increase their reliability for daily, important research works. And when that day comes, if the current route persists, I will stick to Gemini for my deep research needs.