The scoreboard is totally different from what you are seeing in reality. Every week, a new AI model claims to be the best, so people automatically go with it. You see it very well on Twitter.
I think two weeks ago it was Claude AI AI, this week the news is on ChatGPT. People are talking about how ChatGPT can generate the best image. Then coming next week, you get to see another one again.
It feels like progress is accelerating at an impossible pace. And if you are not constantly switching tools, you are risking being left behind. That is not so in reality.
You can stick with just one AI and make the best use of your time. There are some uncomfortable truths that you just have to bear. Those numbers are far less meaningful than they appear.
If you want to understand why, I will help you look at it this way. These scores are created by some AI companies to push and market their ideology. They essentially created a set of questions or tasks designed to measure specific abilities.
And whenever this ability comes to play, trust me, there are other things that you can still do or you are doing that does not even align with it. So you need to know what you are doing and know the best AI for it. One benchmark might test factual recall, another might not.
There are some AI that are good for coding. If you are not doing anything in coding, there is nothing you are doing on your AI platform that is best for coding. Go with the one that goes in flow with what you are doing.
Other major reasoning abilities, the likes of ChatGPT, Gemini and Claude AI. They have a very good reasoning ability and they are problem-solving skills too. Those are all the things that you should know.
The AI that is best for this. If you are doing research very well, publicity will be your best bet. Not ChatGPTT or Claude AI, not even DeepSeek.
The reason is that Perplexity gives you the best information when it comes to research in present time. So there is no need for you running after the latest AI at any point in time. Think of it like a school exam.
A student can ace a practice test because they have seen similar questions before or trained specifically for that format. But when the real exam introduces unfamiliar wordings or different structures, that's when students can struggle. The preparation was optimized for tests, not for understanding.
AI models behave in such a similar way. Some models are trained or fine-tuned specifically to perform well and widely known benchmark. This practice has been done over and over again and is often called teaching to the test.
This is not a harnessed work . But it does create a gap between advertised performance and reward usefulness. If you are writing a proposal, managing customers or conversation, or you are generating content, maybe you are operating within the specific cultural and business content, like how an engineer communicates, negotiates, or expresses nuance.
Those standardized tests don't capture any of that complexity. So you have to be sure that you get the one that resonates with you. A model that excels at academic style reasoning or code completion might completely miss out on intent or local content in practical use.
This has happened several times with a lot of people. You should also know that a model might dominate a leadership board or become very popular in the market. Yet, they produce average and inconsistent or content-blind results.
Then, whenever you are trying to ask people that, oh, why is this happening? They will tell you that it's because you are not using the pro version. No, this is not about the pro version. Not all AI are consistent with whatever they are doing.
Yet, there are so many people that still go ahead and make decisions based on published ranking. They switch tools constantly from this to that because they are trying to chase the AI margin and stuff like that. The smarter approach is simple but requires a shift in mindset.
Stop asserting your judgments to benchmarks you don't design. There are some people that sit down with this and do the marketing based on that. Instead, treat AI tools the way you would treat a new hire or a contractor.
Give them real tasks and ask them. They see their reply. Judge the reply against each other.
Then, stick with your own AI. There is nothing concerning with your social media benchmark that you see online. One practical way to do this is to create a small test suite of your work.
Then, give them the task repeatedly. Then, run it across different AI tools that you are using, be it ChatGPT, Gemini, Claude AI, Perplexity, DeepSeek, Qween. You will quickly see which model resonates or align with whatever you are doing.
These skills, knowing how to evaluate AI tools based on your reality is becoming far more valuable than simply knowing which model is trending. Because when it comes to a landscape flooded with impressive numbers and push comes to shove, the AI that resonates with you might not even be the most popular one.