Which AI is better: GPT 5.2 or Gemini 3? If you have also asked yourself the problem, you are evidently following closely the most recent developments in artificial intelligence from OpenAI and Google. If we had to give you a straight answer, we would tell you that at the moment GPT-5.2 and Gemini 3 satisfy user requests in a very similar way, but with different accents: the former tends to favor precision, operational clarity and support for structured professional work, the latter offers a broader ecosystem and greater integration with tools and services, making it more flexible in daily use.
From the first comparisons it emerges that there is no absolute winner: the performances measured through benchmarks, i.e. standardized tests used to compare different models on the same tasks, show mixed results, with advantages that change depending on the area. Access costs and basic features are also very close. In essence, rather than asking ourselves which is “the best”, it makes sense to ask ourselves which one best responds to our habits and our workflow. It is on this level that the competition between OpenAI and Google becomes really interesting, because it marks the transition from a race for brute power to a phase of maturity, in which the user experience counts as much, if not more, than the numbers and scores achieved in the various tests.
GPT-5.2 and Gemini 3: features and benchmarks compared
GPT-5.2 is the latest evolution of the model underlying ChatGPT and was initially made available to paid users, with an extension of access progressively also available to users of the Free plan. Its arrival comes at a delicate time for OpenAI, which faces the rapid growth of Gemini 3, Google’s most advanced AI model. According to some analyzes circulated on This is a sign of increasingly direct competition from Google. Beyond the market dynamics, however, what is of interest in this in-depth analysis is the technical and functional comparison between the two models.
OpenAI presents GPT-5.2 as particularly effective in the so-called «professional knowledge work». According to OpenAI, in fact, GPT 5.2 would be «better at creating spreadsheets, creating presentations, writing code, perceiving images, understanding long contexts, using tools, and managing complex multi-stage projects». All of these tasks require consistency, multi-step planning, and accurate use of information provided by the user to the model. An initial confirmation of the improvements made by GPT-5.2 comes from LMArenaa platform that collects comparative ratings based on user preferences: here GPT-5.2 achieves relevant results in Web development, surpassing Gemini 3 in this specific category.
If we broaden our gaze, Gemini 3 still dominates many LMArena rankings related to text, images, search and multimedia content, in addition to the general ranking. This reminds us that GPT-5.2 has just arrived and is not yet present in all rankings, while Gemini 3 benefits from greater “seniority” and an already well-tested family of models.
Even the official benchmarks show a balanced scenario. In tests dedicated to software development, advanced mathematics or multimodal understanding, the two models alternate in their results: in some tests GPT-5.2 shows a slight advantage, in others Gemini 3 prevails. It is important to underline, however, that these results are declared by the companies themselves and cannot always be independently verified by third parties (ergo… they take their time).
In terms of functionality, Gemini 3 enjoys a significant advantage as it is integrated into many more products. In addition to the Gemini app, it powers Google tools like Search Engine AI Mode, Google AI Studio, and NotebookLM. This means we can use the same model in different contexts without changing platforms. GPT-5.2, on the other hand, is heavily centered on ChatGPT; It allows the generation of images, but for videos requires the use of a separate app like Sora.
GPT 5.2 vs Gemini 3: who wins in everyday life?
Those who are not fans of benchmarks made by companies (OpenAI and Google in this specific case), therefore, have the arduous task of establishing with practical tests which model is best suited to their needs. Here are two practical tests with which we tried to test and compare the two models to analyze some differences.
Test #1 – Shopping list
In the first test we asked both models to provide us with a shopping list that took into account certain well-defined parameters, using the following prompt:
Act like a savings expert. Create a shopping list for a family of two, taking into account that one adult member is vegan, the other is vegetarian and the two children are omnivores. Furthermore, keep in mind that the budget to be respected for monthly spending is 450 euros.
In the following screenshot you will find part of the output that was returned to us.
Test #2 – Strategy for finding work
In the second test we asked both models to provide us with a strategy that could help an unemployed person find work, using the following prompt:
Act like a professional recruiter. Define a precise strategy that can allow a professional web designer, who has lost his job due to AI, to find a new job that can last over time.
In the following screenshot you will find the output that, in part, was provided to us by the two AIs.
Conclusions
Analyzing the outputs in more detail, however, the differences between GPT-5.2 and Gemini 3 do not concern so much the correctness of the answers, but rather the style, the level of synthesis and the approach to solving the problem.
In the shopping list test, Gemini 3 shows a more narrative and pedagogical approach. Before going into the details of the products, it introduces a general strategy (“common-based cooking”), explains the saving logic and provides numerous collateral advice, such as weekly budget division or batch cooking. The result is a longer and more detailed response, which accompanies the user step by step and is particularly suitable for those who need context, examples and various reassurances. Also very useful is the inclusion of the table to export to Google Sheets, which makes the output truly ready for use.
In the job-finding strategy test, GPT-5.2 adopts a highly operational and results-oriented approach. The response is structured as a true professional roadmap: it identifies the underlying problem, proposes a clear repositioning, suggests niches that can be defended over time and indicates concrete actions to be taken, using language typical of the HR and recruiting world. The almost total absence of preambles and introductory explanations makes the output immediately usable, especially by those looking for practical indications rather than theoretical reflections.
Note: the two tests with which we tested the models are not exhaustive and in other cases may provide results that are not in line with the conclusions we have reached.
