Ask a group of marketers about their favorite AI model, and you’re likely to spark a lively debate. One might argue that ChatGPT is superior, while another insists that Claude writes in a much more natural tone. Meanwhile, someone else might have just discovered that Gemini is actually quite handy. Who is right? Perhaps all of them. After all, what does it really mean when someone claims a particular AI model is the best?
What do those rankings actually tell us?
AI companies love to show off their scores on benchmarks, tests that researchers use to measure how well models can handle tasks like programming, calculation, or logical reasoning. But if you’re looking for a model to answer customer inquiries, what good is a high score on a math test?
In reality, you should test for yourself which AI best suits your specific needs. By giving the same prompt to different AI models, you’ll quickly discover that the responses can vary significantly. Sometimes the differences are subtle, while at other times, a model might take an entirely different approach.
Not every task requires the same AI
Suppose you need to summarize a hundred-page research report; your primary goal is to ensure that the most important information is preserved. A model that produces beautifully crafted sentences but misinterprets half the data is of little use to you.
For a brainstorming session, however, the requirements are quite different. It is perfectly fine for a model to come up with an unexpected idea. In fact, that might be exactly what you are looking for.
There is another factor at play. What we refer to as an AI model in practice is often a complete application. One chatbot might have access to live websites, while another can search through documents. As a result, a model might appear smarter, when in reality it simply has access to different tools.
Testing it yourself is less complicated than you might think
You don’t need to be an AI researcher to conduct a serious comparison of different models. For instance, take twenty customer comments and ask three different models to identify what the customers are complaining about and find high value insights. Before you begin, create your own list of the correct categories.
Then, check how many comments are categorized accurately, but also look at where things go wrong. Does the model understand sarcasm? Can it handle a customer who mentions three separate issues in a single message? A model that operates slightly more slowly but requires almost no manual correction may ultimately prove more cost-effective.
What if two models contradict each other?
This happens more often than you might expect. If you ask two models to evaluate the same report, you may receive different recommendations, sometimes because a prompt is ambiguous, and other times because they prioritize different pieces of information.
Do not simply default to the answer that sounds the most convincing. Investigate the basis for each model’s judgment, and verify whether that reasoning is actually supported by the original report.
