Since early 2025, Artificial Intelligence labs have launched so many new models that it is hard to keep up.
However, the trend indicates that most people only pay attention to ChatGPT.
The new models are impressive, but their names cause confusion. Moreover, evaluation metrics no longer clearly differentiate which one is the best. In short, there are truly advanced AI models on the market, but few people use them.
It is a real shame.
We will analyze the chaos in model naming, the benchmark crisis, and share strategies for choosing the right model according to your needs.
Too Many Models, Confusing Names
The problem with names has been pointed out by industry experts. Companies like Google release multiple versions of the same model with minor tweaks, resulting in complicated and hard-to-distinguish names.
To simplify, models can be classified into different categories: base models, compressed versions through distillation, and specialized models for specific tasks such as advanced reasoning.
Models Are Very Similar in Performance
Determining which model is superior has become a complex task. Classic evaluation metrics have become obsolete, and current performance tests show minimal differences among leading models.
There are various ways to evaluate models:
- Specific benchmarks that measure concrete tasks such as Python programming or accurate response generation.
- Extensive tests that consider multiple metrics, although comparing these numbers can become chaotic.
- Comparison methodologies based on user preferences, similar to an ELO ranking in chess, showing slight advantages for some models in certain scenarios.
The difference between current models is so subtle that switching from one to another may not make a relevant difference in most cases.
How to Choose the Best Model
Given the lack of definitive metrics, the best option is to try different models and evaluate which one best fits individual needs.
Some useful recommendations:
- If the task is new, compare different models in parallel.
- If you already have experience using AI, stick with the model that consistently delivers the best results.
- Do not obsess over benchmark numbers; prioritize the user experience that best suits your workflow.
- If you need concrete data, there are platforms that offer more realistic tests to evaluate models in real-world situations.
- If you develop AI-based products, consider establishing your own evaluation criteria to determine which model works best for your case.
For technology companies like Q2BSTUDIO, specialized in development and technology services, understanding and correctly evaluating these AI trends is essential to offer innovative solutions tailored to market needs.
The evolution of AI models will continue to change constantly, and choosing the right one will depend more on user experience and specific needs than on a simple numerical comparison.
