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:

1. **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.

2. **Data Contamination.** AI models are trained on massive datasets scraped from the internet. If the questions from a benchmark are floating around online, 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.

3. **Goodhart's Law.** 'When a measure becomes a target, it ceases to be a good measure.' When AI companies optimize their training specifically to pass popular benchmarks like MMLU or HumanEval, the benchmark loses its ability to measure true general intelligence.