AI Benchmarks Explained

The variety of different AI models is increasing every day. It feels like every week there's a new model drop from OpenAI, Google, Anthropic, or some company you've barely heard of. With so many options out there, how can you actually know which ones are the best? The answer: benchmarks. But what is a benchmark, how do they work, and how much should you trust them? For the answers to those questions and more, keep reading.

What Is A Benchmark?

A benchmark is a way to test an AI model's capabilities in a certain field. Think of it like a standardized test, but for AI. Some benchmarks test programming ability, others test a model's knowledge in specific scientific fields, and others test reasoning or math. There are benchmarks for just about everything you could want an AI to do.

For example, TerminalBench 2.1 tests how well an AI can run terminal commands. GPQA is a multiple choice benchmark that tests graduate level biology, chemistry, and physics knowledge. AIME tests mathematical problem solving. HumanEval tests code generation. The list goes on and on, and it keeps growing.

Usually, benchmarks are measured in percent of questions answered correctly. So if a model scores 85% on GPQA, that means it got 85% of the questions right. Simple enough. When a new model launches, the company behind it will almost always publish a table of benchmark scores showing how their model stacks up against the competition. These numbers are what headlines are made of, and they're often what people use to decide which model is the best.

But these numbers don't always tell the full story.

Why Benchmarks Can Be Misleading

This type of benchmark, where a model answers a fixed set of questions and gets a score, isn't always as accurate as it looks. There are a few reasons for this:

The benchmarks don't always test what the user actually does. This is probably the most common issue. A benchmark might test an AI's capability in frontier level math, the kind of stuff that would challenge a PhD student, and give it a very high score. Great, right? But then you ask for it to do a high school level math problem, and it confidently makes up an answer. The benchmark score looked impressive, but it didn't reflect how the model actually performs on the tasks real people need it for. The same thing happens with coding benchmarks. A model might crush a benchmark full of self-contained algorithm puzzles, then struggle when you ask it to work within a large, messy, real-world codebase. Benchmarks often test narrow, isolated skills, while actual usage is a lot messier and more varied.

During training, the benchmark questions were sometimes given to the AI, intentionally or not. This is called data contamination, and it's a big deal. AI models are trained on massive datasets scraped from the internet. If the questions from a benchmark are floating around online, and many of them are, there's a real chance the model has seen them during training. That means the model isn't actually solving the problem, it's just recalling the answer. It's like giving a student the answer key before the test. Some companies have been accused of doing this on purpose to inflate their scores, but even when it's not intentional, it still happens. And when it does, the benchmark score becomes meaningless, because it's no longer measuring the model's actual ability to reason or problem solve.

The benchmarks themselves are flawed. Some benchmarks have broken questions, where the correct answer is actually wrong, or the question is worded so poorly that multiple answers could be right. Others are just vague and open to interpretation. When the benchmark itself has issues, the scores it produces are unreliable no matter how carefully the models are tested. And because many popular benchmarks have been around for a while, some are starting to show their age. Models have gotten so good that they're hitting the ceiling on older benchmarks, scoring 90%+ across the board, which makes it hard to tell the top models apart.

Benchmarks can be gamed in other ways too. Companies can fine-tune their models specifically to perform well on popular benchmarks without actually improving general capability. They might optimize for the format of the questions, the types of problems asked, or even the specific distribution of topics. The result is a model that looks great on paper but doesn't feel noticeably better when you're actually using it. This is basically teaching to the test, and it's just as much of a problem in AI as it is in education.

How The Industry Is Trying to Fix This

To solve some of these problems, new benchmarks are constantly being created. The idea is that fresh benchmarks have fresh questions the models haven't been trained on, which makes the results more trustworthy. For example, DeepSWE is a newer coding benchmark that's a lot more accurate than SWE-Bench Pro when it comes to testing real software engineering ability. By regularly introducing new benchmarks and retiring old ones, the community tries to keep datasets from being corrupted by contamination.

Some newer benchmarks also try to be more practical and realistic. Instead of asking isolated multiple choice questions, they give models complex, multi-step tasks closer to what a real user would actually ask. This helps bridge the gap between benchmark performance and real world usefulness.

There's also been a push to make benchmarks harder and more adversarial, designed to find the gaps in a model's knowledge rather than confirm what it already does well. These benchmarks are less likely to be saturated, so they can still tell the top models apart.

The Most Accurate Way to Test an AI

Still, despite all the effort that goes into building better benchmarks, AI is best tested by the users themselves. No fixed set of questions can fully capture how useful, helpful, or reliable a model is across the wide range of things people actually use AI for.

That's where user-driven benchmarks come in. The best example is probably Arena AI's ELO score. Here's how it works: users enter a prompt and get two different responses from two different models, without knowing which model generated which. Then they pick which response is better. Over thousands and thousands of these matchups, models earn an ELO rating, the same kind of rating system used in chess and other games, that reflects how often real humans preferred their responses.

This is probably the most accurate way to see which AI model is the best, for a few reasons. First, the prompts come from real users, so they reflect real use cases, not just academic puzzles. Second, the evaluation is done by humans, not an automated scoring system that might miss nuance. Third, companies can't really train their models specifically to perform well on this benchmark, because there's no fixed dataset to optimize for. The only way to rank higher on Chatbot Arena is to actually make your model better at helping people. That's exactly the kind of incentive you want a benchmark to create.

It's not perfect. User preferences can be biased, people tend to prefer longer, more detailed responses even when a shorter answer is better, and the demographics of Arena users may not represent everyone. But it's still a lot more meaningful than most traditional benchmarks out there.

Make Your Own Benchmark

One more thing worth mentioning: you can create your own benchmarks. If you use AI regularly for specific tasks, make a list of prompts relevant to what you actually do. Give the same prompts to different models and compare the responses. See which one gives you the most useful, accurate, and well-written answers for your specific needs.

At the end of the day, no public benchmark can tell you which model is best for you. A model that ranks #1 on every leaderboard might not fit your workflow at all. The only way to really know is to test it yourself. Your own experience will always be more valuable than a number on a chart.