LLM benchmarking: How to find the right AI model

Today, there is hardly any way around AI. But how do companies decide which large language model (LLM) is right for them? The choice is currently wider than ever, the possibilities seemingly endless. But beneath the glossy surface of advertising promises lurks the crucial question: Which of these technologies really delivers what it promises — and which ones are more likely to cause AI projects to falter?

LLM benchmarks could be the answer. They provide a yardstick that helps user companies better evaluate and classify the major language models. Factors such as precision, reliability, and the ability to perform convincingly in practice are taken into account.

LLM benchmarks are the measuring instrument of the AI world. These are standardized tests that have been specifically developed to evaluate the performance of language models. They not only test whether a model works, but also how well it performs its tasks.

The value of benchmarks lies in their ability to bring order to the diversity of models. They reveal the strengths and weaknesses of a model, enable it to be compared with others and thus create the basis for informed decisions. Whether it’s about selecting a chatbot for customer service, translating scientific texts or programming software, benchmarks provide an initial answer to the question: Is this model suitable for my use case?

The most important findings at a glance:

• Versatility: Benchmarks measure a wide range of skills, from language comprehension to mathematical problem solving and programming skills.
• Specialization: Some benchmarks, such as MultiMedQA, focus on specific application areas to evaluate the suitability of a model in sensitive or highly complex contexts.
• Challenges: Limitations such as data contamination, rapid obsolescence and limited generalizability require critical understanding when interpreting the results.

The 3 pillars of benchmarking

Benchmarking is based on three pillars:

Data sets

Data sets form the basis of the tests: data sets are collections of tasks and scenarios that have been specifically developed to test the abilities of language models. They define the challenges that a model has to overcome.

The quality and diversity of the data sets used is crucial to the validity of a benchmark. The better they simulate real-world applications, the more useful and meaningful the results are.

One example is SQuAD (Stanford Question Answering Dataset), which provides text passages and associated questions to test whether a model can extract relevant information from the passages.

Evaluation

Evaluation methods assess the performance of the models. Evaluation: While data sets define the tasks, the performance of a model is measured by evaluation methods. There are two main approaches:

• Reference-based metrics: These metrics compare the generated response of a model with an ideal reference text. A classic example is BLEU, which measures how closely the word sequences in the generated response match those of the reference text. BERTScore goes one step further by not only evaluating word matches but also analyzing semantic similarity. This is especially useful when meaning is more important than literal accuracy.
• Reference-free metrics: These metrics evaluate the quality of a generated text independently of a reference. Instead, they analyze the coherence, logic, and completeness of the response on its own. For example, a model might summarize the source text, “Climate change is one of the most pressing issues of our time. It is caused by the increase in greenhouse gases such as CO₂, which mainly come from the combustion of fossil fuels.” with ‘Climate change is caused by CO₂ emissions.’ A reference-free metric would check whether this summary correctly reflects the essential content and remains logical in itself.
• LLM-as-a-Judge — AI as an evaluator: An innovative approach in the evaluation of large language models is to use the models themselves as their own “judges”. In the “LLM-as-a-Judge” concept, these models analyze both their own answers and those of others and evaluate them based on predefined criteria. This approach enables new possibilities that go beyond classic metrics. However, there are also challenges: one study showed that models tend to recognize their own answers and rate them better than those of others. Such biases require additional control mechanisms to ensure objectivity. Research in this area is still in its infancy, but the potential for more accurate and nuanced evaluations is great.

Rankings

Rankings make results transparent and comparable: Rankings provide a valuable overview of the benchmark results of large language models. They make the performance of different models comparable at a glance and thus promote transparency. Platforms like Hugging Face or Papers with Code are good places to start.

But be careful: a top position in a ranking should not be confused with universal superiority. The selection of the right model should always be based on the individual requirements of a project.

Common LLM benchmarks by category

The world of LLM benchmarks is constantly evolving. With each advance in the LLMs themselves, new tests are created to meet the increasing demands. Typically, benchmarks are designed for specific tasks such as logical thinking, mathematical problem solving or programming. Some well-known benchmarks are presented below:

Reasoning and language comprehension

• MMLU (Massive Multitask Language Understanding): This benchmark tests a model’s breadth of knowledge across 57 academic and professional disciplines. With nearly 16,000 multiple-choice questions based on curricula and exams, topics such as mathematics, medicine and philosophy are covered. A particular focus is on complex, subject-specific content that requires advanced knowledge and logical reasoning. Paper: Measuring Massive Multitask Language Understanding
• HellaSwag: HellaSwag measures a model’s common sense understanding by selecting the most plausible follow-up sentence from four options. The tasks were designed to be easy for humans but difficult for models, making this benchmark particularly challenging. Paper: HellaSwag: Can a Machine Really Finish Your Sentence?
• TruthfulQA: This benchmark assesses a model’s ability to provide truthful answers without reproducing misunderstandings or false assumptions. With 817 questions in 38 categories, including law and health, TruthfulQA is specifically designed to uncover widespread misinformation. Paper: TruthfulQA: Measuring How Models Mimic Human Falsehoods

Mathematical problem solving

• MATH: MATH includes 12,500 mathematical tasks from areas such as algebra, geometry and number theory. Each task is annotated with a step-by-step solution that allows for a precise evaluation of problem-solving skills. The benchmark tests a model’s ability to recognize logical relationships and provide mathematical precision.Paper: Measuring Mathematical Problem Solving With the MATH Dataset

Programming skills

• HumanEval: HumanEval offers 164 Python programming tasks with comprehensive unit tests to validate the solutions. The benchmark tests the ability of a model to generate functional and logical code from natural language descriptions. Paper: Evaluating Large Language Models Trained on Code

Domain-specific benchmarks

• MultiMedQA: MultiMedQA combines six medical datasets, including PubMedQA and MedQA, to test the applicability of models in medical contexts. The variety of questions — from open-ended to multiple-choice tasks — provides a detailed analysis of domain-specific abilities. Paper: Large language models encode clinical knowledge

Special benchmarks

• MT-Bench: MT-Bench focuses on the ability of language models to provide consistent and coherent responses in multi-step dialogs. With nearly 1400 dialogs covering topics such as math, writing, role-playing, and logical reasoning, the benchmark provides a comprehensive analysis of dialog capabilities. Paper: MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
• Chatbot Arena: Chatbot Arena is a platform that allows for direct comparison between models. Users can test anonymized chatbots by evaluating their responses in real time. The Elo rating system is used to create a dynamic ranking that reflects the performance of the models. The benchmark stands out due to its crowdsourcing approach. Anyone can contribute to the benchmark at Chatbot Arena. Paper: Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
• SafetyBench: SafetyBench is the first comprehensive benchmark to examine the safety aspects of large language models. With over 11,000 questions in seven categories — including bias, ethics, potential risks and robustness — it provides a detailed analysis of the safety of models. Paper: SafetyBench: Evaluating the Safety of Large Language Models

Even benchmarks have their limits

Despite their enormous importance, benchmarks are not perfect tools. While they provide valuable insights into the capabilities of language models, their results should always be critically analyzed.

One of the biggest challenges is what is known as data contamination. Benchmarks derive their validity from the assumption that models solve tasks without prior exposure. However, a model’s training data often already contains tasks or questions that match the data sets. This can make the results appear artificially better than they would in reality and distort the actual performance of a 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.

• Define clear requirements: First, you should consider which skills are crucial for the specific project. Accordingly, benchmarks are selected that cover these specific requirements.
• Combine multiple benchmarks: No single benchmark can evaluate all the relevant capabilities of a model. A combination of different tests provides a differentiated performance picture.
• Weight benchmarks: By defining priorities, the benchmarks that have the greatest influence on the success of the project can be selected.
• Supplement benchmarks with practical tests: Using realistic tests with real data can ensure that a model meets the requirements of the specific application.
• Stay flexible: New benchmarks are constantly being developed that are better able to reflect the latest advances in AI research. It pays to stay up to date here.

With the strategic use of benchmarks, not only can a better model be chosen, but innovation potential can also be fully exploited. However, benchmarks are only the first step — the real skill lies in integrating and adapting models into real applications.

Annika Schilk works as a consultant in adesso SE‘s public sector. Her focus is on data and AI, especially on natural language processing. She is currently working intensively with GenAI.

Ramazan Zeybek is a working student in consulting at adesso, with a focus on AI and data analytics. His focus is on the preparation, visualization and analysis of data, as well as the automation of data-driven processes. At the same time, he is studying business informatics at the University of Hamburg.