AI Benchmarks: How to measure real progress in artificial intelligence
AI Benchmarks: How to Measure Real Progress in Artificial Intelligence
AI systems are advancing quickly, but measuring their abilities is not straightforward. A model that performs impressively in one setting may fail to perform as well in another. Benchmarks provide a structured way to evaluate how well an AI system performs the tasks for which it was designed.
In the article, we’ll explore why benchmarks matter, what separates strong benchmarks from weak ones, and how Toloka has developed its own evaluation frameworks to meet these standards.
Why benchmarks matter in AI
Artificial intelligence has advanced at an astonishing pace over the past decade. But with this rapid progress comes a natural question: how do we know whether one system is actually better than another?
Benchmarks provide part of the answer. They act as shared reference points that researchers and companies can use to measure performance, compare models side by side, and reveal strengths or weaknesses that aren’t obvious. A well-designed benchmark doesn’t just assign a score; it paints a more detailed picture of what a model can and cannot do.
An AI benchmark is a standardized method for evaluating the performance of an artificial intelligence system in a specific task. It usually takes the form of a dataset, a set of questions or prompts, and a method for scoring the system’s answers. The goal is to create a consistent frame of reference: when two or more models are evaluated with the same benchmark, their results can be compared in a fair and meaningful way.
In practice, a benchmark might involve classifying images, answering reading comprehension questions, translating text, or solving math problems. Each is designed to capture a specific ability. For example, the long-standing ImageNet benchmark pushed computer vision forward by testing whether models could correctly recognize thousands of everyday objects in photos. More recently, benchmarks for large language models test reasoning, coding, or multilingual understanding.
Other industries rely on similar methods. Standardized crash tests evaluate cars, new medicines must undergo clinical trials, and students demonstrate their abilities with exams. Without these standard evaluation tools, it would be nearly impossible to tell which product or method truly performs best. AI, with its rapid pace of innovation and high stakes for deployment, requires the same kind of yardsticks.
The challenge is that not all benchmarks serve this purpose equally well. Some have become outdated, others focus too narrowly, and a few even encourage “shortcuts” that make models look smarter than they really are. Understanding what makes a benchmark genuinely helpful is the first step toward ensuring AI systems are tested in ways that reflect their real capabilities.
Benchmarks as “exams” for AI systems
One of the most helpful ways to think about benchmarks is to see them as exams for AI. Just as students sit down to take a test in mathematics, literature, or history, AI models are “tested” on specific tasks designed to reveal how well they can perform. The benchmark establishes the rules of the exam, including the questions, grading system, and conditions under which the model is evaluated.
This analogy also reveals the strengths and pitfalls of benchmarking. A well-crafted exam shows not just what a student memorized, but whether they can apply knowledge to solve new problems. A poorly designed exam, on the other hand, may reward rote memorization, test only a narrow slice of skills, or fail to reflect real-world application. The same applies to AI benchmarks.
The aforementioned ImageNet challenge has served as the de facto exam for computer vision systems for years. It spurred enormous progress, but eventually, models learned to ace the test without necessarily comprehending images in a human-like way. In other words, they became excellent at passing that exam, but not necessarily at general vision tasks.
This is why AI needs benchmarks that do more than check a box. The right kind of benchmark acts like a comprehensive exam. The one that measures reasoning, adaptability, and robustness, rather than rewarding shallow tricks. As AI systems become more capable, benchmarks must evolve to test higher-order skills, much like education shifts from multiple-choice quizzes to more complex assessments, such as essays, projects, or practical demonstrations.
It’s essential to remember that a benchmark is not intended to encompass everything an AI system can do. Just as a single exam doesn’t define a student’s entire intelligence, a benchmark highlights performance in a focused area. Over time, researchers rely on collections of benchmarks that cover various domains and skills to gain a broader understanding of a system's capabilities.
What makes a good AI benchmark
So, what separates a good AI benchmark from a bad one? Just like with academic exams, it’s all about design. A meaningful benchmark is one that measures the right abilities, in the right way, for the right reasons. Below are the key criteria that define a benchmark worth trusting.
Clear purpose & scope
Every AI benchmark should begin with a clear definition of what it is testing and why it is being tested. Without this, results can be misleading. Is the benchmark meant to evaluate a model’s reasoning? Its ability to handle multiple languages? Or perhaps it's skill in processing multimodal input, such as text, images, and audio, together?
Stanford’s Institute for Human-Centered AI describes a high-quality AI benchmark as "clear about its intended purpose and scope". Just as you wouldn’t create a math exam with random geography questions sprinkled in, an AI benchmark should avoid being vague or overly broad. Narrow but purposeful design ensures that when a model scores well, we actually learn something specific about its abilities.
Validity (Measuring what matters)
A benchmark is only valuable if it tests what it claims to test. Imagine giving a student a task that only requires them to memorize dates. They may get a high score, but you’ve learned nothing about their reasoning ability. AI benchmarks face a similar challenge.
For instance, if an AI benchmark is designed to test reasoning in large language models, it should include problems that require step-by-step logic, rather than simply recalling facts from the training data. Contamination occurs when benchmark data leaks into model training datasets, posing a real risk that makes models appear to perform well when, in fact, they’re merely repeating what they’ve already seen. Avoiding this is crucial for maintaining the validity of the results.
A valid benchmark focuses on the ability that genuinely matters, not on shortcuts that models can exploit. In practice, that often means designing tasks that require fresh thinking, a combination of knowledge, or transfer to new contexts, rather than regurgitation.
Diversity & representativeness
Strong benchmarks need to reflect the diversity of real-world use cases. Modern artificial intelligence systems process text, images, and speech, so assessments that cover multiple modalities give a deeper level of comprehension than assessments that focus on just one. If benchmarks remain narrow, we miss essential trends in how models adapt in their tasks.
Language is another factor. While English often dominates in AI research, models are increasingly expected to work with many languages and dialects. A benchmark that includes this variety allows us to assess whether systems generalize well or only succeed in high-resource settings. Without that coverage, the results can give an incomplete and less accurate picture of capability.
Diversity also applies to domains and difficulty levels. Some tasks are simple and lead to high average scores, but these alone don’t reflect the quality of a system’s reasoning. A good benchmark combines easy and difficult examples, allowing us to determine better where a model excels and where it struggles.
Robustness
Benchmarks also need to be robust, ensuring that models can’t exploit shortcuts. In practical use cases, AI encounters noise, ambiguity, or even deliberately misleading input. Testing only clean data leads to inflated metrics and poor real-world transfer.
Robust benchmarks introduce edge cases and adversarial challenges to evaluate whether systems remain reliable under pressure. These design choices reveal weaknesses that simple datasets might hide and provide insights that guide further research.
Another aspect of robustness is ensuring that models don’t simply memorize the test, in other words, avoiding overfitting. If the same benchmark version is used for too long, models can “overfit” to it and start achieving high scores without actually improving their performance in the underlying skill. To prevent this, benchmarks need fresh versions or new tasks once older ones become saturated. This way, results remain meaningful and comparisons over time reflect real progress rather than test familiarity.
Reproducibility & reliability
An AI benchmark is only valuable if its outcomes can be trusted. That means the evaluation pipeline must be standardized so that different teams can run the same test and achieve comparable results. Consistency is what allows a fair comparison between AI models.
For models that don’t always produce the same output, a single test run can give a misleading impression. To reduce this randomness, AI benchmarks usually run the same task several times and then calculate the average result. This provides a more accurate representation of the model's overall performance. Reliable metrics are crucial here because they enable researchers to assess not only the final score but also the consistency of the model across different runs.
Accessibility is just as important as accuracy. If running an AI benchmark is too expensive or complicated, only a few people will be able to use it. By lowering the cost and making access easier, benchmarks become available to a broader range of users, from researchers and developers to everyday practitioners.
Transparency & documentation
A good benchmark doesn’t hide its inner workings. Clear documentation about datasets, including their sources, licenses, and annotation process, gives users confidence in the quality of the evaluation. Just as important are transparent metrics and protocols so that researchers can accurately determine how results were produced and make fair comparisons of different AI models. This level of clarity makes later analysis more meaningful and reliable.
Fairness & inclusivity
AI benchmarks should reflect the diversity of the world. That means avoiding bias toward specific languages, cultures, or demographics, and ensuring that low-resource settings are also supported. By including underrepresented groups and domains, benchmarks produce outputs that are more balanced and more applicable to real-world applications.
Human involvement where needed
Not every task can be entirely judged by automated metrics. For subjective or complex outputs, such as creative writing or nuanced reasoning, AI benchmarks should include human evaluators to ensure accuracy and reliability. Their assessments help ensure that quality is measured in ways machines alone cannot capture.
Maintenance & evolution
AI evolves quickly, and so should benchmarks. Over time, tasks may become too easy, with AI models reaching near-perfect scores. At that point, a benchmark risks losing its value. Regular updates or even sunsetting old versions keep benchmarks relevant, cost-effective, and aligned with current research trends. Each new version should not only test performance but also guide deeper analysis of how models are improving.
Toloka’s benchmarks overview
Toloka allows teams to collect high-quality, annotated data, making it usable for training and testing AI systems in realistic settings. Additionally, it provides a range of benchmarks designed to evaluate AI systems across various domains and modalities. Each benchmark is constructed in a clear format that is easy to understand and apply, regardless of whether the task involves text, images, or speech as the primary medium.
Some of Toloka’s benchmarks are available for free for research purposes, which lowers the barrier for both individual users and organizations to experiment with and evaluate their models. Toloka provides datasets and open-source scripts to simplify integration. Benchmarks like Beemo, U-MATH/μ-MATH, and JEEM present tasks that models need to solve under real-world constraints. Some challenges are intentionally complex, reflecting the complexity AI systems encounter in real applications.
How Toloka benchmarks fit the criteria for good benchmarks
Beemo focuses on AI detection in texts with mixed authorship. It has a clear purpose, measuring how well models can distinguish human-edited from AI-generated text at varying edit levels. Its validity is strong because it tests realistic detection scenarios rather than idealized AI text. The benchmark includes multiple authorship mixes to promote diversity, and its tasks are robust, with even subtle edits capable of challenging models. Beemo is reproducible and reliable, publicly available with standardized evaluation, and transparent, providing clear documentation of text types and editing processes.
U-MATH and μ-MATH evaluate mathematical reasoning at the university level. They test proper problem-solving skills rather than memorization, covering six math domains with some visual problems to ensure representativeness. Problems vary in difficulty to test robustness, and clear scoring protocols with human-verified solutions ensure reproducibility and reliability. Dataset descriptions and task documentation provide transparency.
JEEM assesses vision-language models in Arabic dialects and Modern Standard Arabic. Its purpose is to evaluate captioning and question-answering abilities in realistic, culturally relevant contexts. JEEM ensures diversity by including multiple dialects and multimodal inputs (text and images), and tasks are designed to be challenging in low-resource dialects. Reproducibility is supported through documented dataset creation and multiple evaluation protocols, while transparency is maintained with clear annotation guidelines and cultural filtering.
Together, these benchmarks demonstrate how Toloka aligns with the main criteria of high-quality AI benchmarks: clear purpose, validity, diversity, robustness, reproducibility, and transparency.
