Why impression is not enough
It is tempting to choose a language model based on how its answers feel in a few trials. This is unreliable, however: a handful of examples does not tell you how the model performs across hundreds of real cases, nor does it reveal where the model consistently fails. A reliable choice requires systematic evaluation in which models are compared on the same test set with the same metrics.
Build your own test set
General benchmarks tell only part of the truth, because they do not match your specific use case. The best evaluation rests on your own test set: assemble a set of real questions and their correct answers from your own domain. The closer the test set is to real use, the more reliably it predicts how the model will perform in production. This set is also valuable when models or model versions change.
What to measure
The important metrics depend on the task, but common ones are: correctness (is the answer factually right), relevance (does it answer the question), consistency (does the quality hold across similar inputs) and safety (are harmful or misleading answers avoided). In a multilingual environment you also measure whether the model performs equally well in all the required languages. Metrics should be defined before testing so that the evaluation is honest.
Automation and human review
Part of the evaluation can be automated: compare the model answer to the correct answer, or use another model as a judge. This scales well. In critical cases, however, human review is also needed, because a person notices nuances that an automated metric does not catch. Best practice combines both: automation for broad screening and a human for the most important decisions.
Evaluation is continuous
Models and their versions update continuously, and the same model can behave differently at different times. That is why evaluation is done not once but repeatedly. With the test set and metrics in place, evaluating a new model or version is quick, and you can make decisions on numbers rather than gut feel. This is the same discipline we apply to all production solutions.
Summary
The choice of a language model must rest on measurement, not impression. Build your own test set, define clear metrics, combine automation and human review, and repeat the evaluation regularly. That way you know which model truly fits your task, and you can justify your choice with numbers.