LLM benchmarking: How to find the right AI model

In addition, many benchmarks quickly become outdated. The rapid development in AI technology means that models are becoming more and more powerful and can easily handle tests that were once challenging. Benchmarks that were previously considered the standard thus quickly lose their relevance. This requires the continuous development of new and more demanding tests to meaningfully evaluate the current capabilities of modern models.

Another aspect is the limited generalizability of benchmarks. They usually measure isolated abilities such as translation or mathematical problem-solving. However, a model that performs well in a benchmark is not automatically suitable for use in real, complex scenarios in which several abilities are required at the same time. Such applications reveal that benchmarks provide helpful information, but do not reflect the whole reality.

Practical tips for your next project

Benchmarks are more than just tests — they form the basis for informed decisions when dealing with large language models. They enable the strengths and weaknesses of a model to be systematically analyzed, the best options for specific use cases to be identified, and project risks to be minimized. The following points will help you to implement this in practice.