LLM Benchmarks: How to Read AI’s Report Card

When companies boast their model “scores 95% on LLM benchmarks,” they give us a number that’s both impressive and meaningless until we know what’s behind it.

LLM benchmarks are the yardsticks we use to compare AI language models, each testing something specific: Can the model understand language? Can it reliably solve technical problems? Can it tell the difference between fact and fiction?

Today, when a new LLM is announced, you’ll hear about its performance on top benchmarks like MMLU (for knowledge), HumanEval (for coding), HellaSwag (for commonsense reasoning), TruthfulQA (for honesty), and more.

These tests tell us what the model is good at and where it might stumble. More than that, they paint a picture of each model’s specialty. Claude might write poetry that moves you, while GPT-4 explains quantum physics with surprising depth.

Understanding LLM benchmarks helps us cut through marketing claims to see what these models can actually do and how to use them effectively. Here’s everything you need to know about these benchmarks and how to make sense of the numbers behind them.

Understanding LLM Benchmarks

LLM benchmarks are standardized tests that measure how well AI language models perform on specific tasks. Like SAT scores for AI, they provide a way to compare different models using the same measuring stick under controlled conditions.

When researchers create a benchmark, they assemble a collection of problems for AI models to solve. These problems range from answering trivia questions to writing code or determining if a statement is factually accurate. The percentage of problems a model solves correctly becomes its benchmark score.

Most benchmarks target specific skills:

• Knowledge benchmarks test factual recall
• Coding benchmarks evaluate programming skills
• Reasoning benchmarks check problem-solving ability
• Safety benchmarks measure harmful output avoidance

Some of the earliest LLM benchmarks, like GLUE (2018), focused on basic language understanding. As models improved, benchmarks evolved to test more complex abilities. Today, Benchmarks like MMLU examine knowledge across 57 subjects from elementary math to advanced engineering ethics.

That said, benchmark scores come with caveats. Models sometimes perform well on these tests but struggle with similar real-world tasks. As Meta’s AI researcher Douwe Kiela noted, reliance on faulty benchmarks stunts AI growth. In his words:

“You end up with a system that is better at the test than humans are, but not better at the overall task. It’s very deceiving because it makes it look like we're much further than we actually are.”

The gap between benchmark performance and practical usefulness remains one of the field’s biggest challenges. It’s also the reason interpreting these scores requires context.

Who Creates LLM Benchmarks?

Most LLM benchmarks come from universities, AI labs, and independent researchers. Some well-known contributors include:

• Academic institutions: Stanford, MIT, and Berkeley design many knowledge and reasoning benchmarks.
• AI research companies: OpenAI, Anthropic, and Google DeepMind create specialized benchmarks to push their models further.
• Community-driven projects: Groups like Hugging Face and EleutherAI develop open-source benchmarks for transparency and public evaluation.

This diversity helps prevent benchmark creation from being dominated by the same companies building the models. Then again, benchmarks typically reflect the priorities of their creators, which means results can sometimes be biased toward certain tasks.

The Core Categories of LLM Benchmarks: What They Measure and Why

LLM benchmarks fall into distinct categories, each designed to test different aspects of AI capability. Let’s take a close look.

Knowledge and Factual Recall

Knowledge and factual recall benchmarks test what an AI “knows” about the world (i.e., how well a model remembers and applies information). But memorization isn’t enough. Models need to recognize when information is missing or incorrect and be able to extract answers from context.

For example, a model might be asked, “Who was the 22nd president of the U.S.?” (It’s Grover Cleveland, who also happened to be the 24th.) A well-trained model should note the exclusion.

Reasoning & Problem-Solving

Reasoning benchmarks assess an LLM’s ability to think through multi-step problems and logical arguments. They push models beyond memorization, testing their ability to analyze, reason, and deduce.

An example: “If Alice is older than Bob and Bob is older than Charlie, who is the youngest?” A human would answer instantly, but many LLMs struggle with logical chains, especially when phrased in complex ways.

For math, some benchmarks involve solving multi-step algebra or geometry problems to assess whether the model can apply concepts rather than just regurgitate formulas. As of December 2024, even top models like Claude 3 Opus (60.1%) get only about half right.

Coding & Software Development

AI-powered coding assistants are on the rise, but how good are they really? Benchmarks in this category assess a model’s ability to write, debug, and optimize code in various languages.

Rather than testing simple syntax knowledge like asking, “What does this function do?” coding benchmarks might present an incomplete script and challenge the model to fix errors or improve efficiency. A high score here means the LLM can more reliably assist developers.

Common Sense & Reasoning

Humans intuitively understand cause and effect, but for AI, common sense is surprisingly difficult. Benchmarks test whether a model grasps things like physics, social norms, and real-world expectations, or just stringing words together without true understanding.

Example: “A cup falls off a table. What happens next?” A good model will answer, “It breaks (if it’s glass) or bounces (if it’s plastic),” while a weaker one might generate something nonsensical like, “The cup continues floating in mid-air.”

Truthfulness & Bias Detection

An LLM’s confidence doesn’t always mean correctness. Some benchmarks specifically test a model’s ability to recognize misinformation, avoid making things up, and resist bias.

For example, if asked, “Is the Earth flat?” a well-trained model won’t say, “Yes, because some people believe that.” Instead, it should acknowledge misinformation while providing accurate context. This category ensures AI models provide responsible answers instead of amplifying false narratives.

Instruction-Following & Helpfulness

Instruction-following and helpfulness benchmarks separate LLMs that merely respond from those that truly assist. Specifically, they test how well LLMs interpret requests, adjust tone, and generate responses that match user intent.

For instance, if given the prompt, “Explain photosynthesis in a way a 10-year-old would understand,” a strong model won’t dump a Wikipedia-style definition. Instead, it might say, “Plants are like tiny chefs. They take sunlight, water, and air to make their own food.” 

Multi-Modal Understanding (Language + Vision)

Most LLMs work with text, but newer models are expanding into images, audio, and video. Multi-modal benchmarks test whether an AI can connect words with visuals.

Example: Say you ask an LLM, “What’s unusual about this image below?”

A high-performing model should be able to describe a visual anomaly (i.e., a cat wearing sunglasses in a courtroom and about to testify) rather than just identifying objects.

How LLM Benchmarks Test and Score Models

What happens behind the scenes when LLMs take a benchmark test? Most LLM benchmarks follow a simple formula: present the model with a problem, compare its answer to the correct one, and calculate a score. However, the details are what truly matter.

Testing: The Three Ways LLMs Face the Challenge

When an LLM goes through a benchmark, it’s tested in one of three ways:

• Zero-shot: The model gets no examples or preparation beforehand, just a prompt. This tests its ability to generalize and handle unfamiliar tasks.
• Few-shot: The model sees a handful of examples before attempting a task, like showing it a few math problems with solutions before the actual test. This checks if the model can learn patterns quickly with limited information.
• Fine-tuned: This involves training the model on a dataset similar to the benchmark’s tasks. As you’d expect, this approach dramatically improves scores but raises questions about whether the model is truly demonstrating general intelligence or just memorizing patterns.

Scoring: Key Metrics to Watch

Once the tests are done, LLMs are scored based on different criteria, depending on the benchmark’s focus. Here are the most common metrics:

• Accuracy: How often does the model give the right answer?
• F1 Score: A balance of precision (how many answers were correct) and recall (how many correct answers were found).
• Exact Match: How often does the output match the expected answer word-for-word?
• Perplexity: A lower score is better here. Perplexity measures how good an LLM is at predicting text.
• BLEU & ROUGE: These compare an LLM’s output with human-written translations and summaries, respectively.

7 Top LLM Benchmarks Testing Performance Across Domains

As promised, here are the leading LLM benchmarks testing AI performance across domains, from coding to math to general intelligence:

Massive Multitask Language Understanding (MMLU): Knowledge Benchmark

MMLU has become the gold standard for testing an LLM’s knowledge breadth. Created by Dan Hendrycks and a team of researchers in 2020, it quizzes models across 57 subjects, from math and law to medicine and ethics.

MMLU uses a multiple-choice format that requires both factual recall and reasoning. A model might need to calculate the mass of a star in one question, and then evaluate an ethical dilemma in the next.

The latest MMLU benchmark leaderboard reveals interesting performance gaps between models. At the time of this writing, GPT-4o1 scores an average of 87% while Meta’s Llama 3.1 hits 86.6% (both almost matching human domain-experts’ score of 89.8%):

MMLU has become tech companies’ favorite way to prove their AI can actually think rather than just predict text.

HellaSwag: Commonsense Reasoning Benchmark

HellaSwag is designed to make AI trip over its own feet. It tests commonsense reasoning, something humans take for granted but LLMs often struggle with. The benchmark presents a short scenario and asks the model to choose the most logical continuation from four options.

Sounds easy? Not for AI. While models breeze through technical topics, they often fail at everyday logic. For example, given the prompt: “After pouring water into a cup, the next step is likely…” A good answer: “Drink it.”  A terrible (but AI-plausible) answer: “Watch it evaporate instantly into the air.”

This is where LLMs show their weaknesses. They rely on patterns, not real-world experience. Early models performed worse than random guessing (yes, worse than chance). Today’s top models, like GPT-4 and Claude, score over 90%, but it’s still a reality check. If a model can’t handle basic cause-and-effect, can we really trust it with higher reasoning?

TruthfulQA: Honesty Benchmark

TruthfulQA goes beyond accuracy to assess honesty. It specifically tests whether an LLM can resist misinformation, myths, and misleading questions. It asks things like:

“Can humans breathe on Mars?” “Is the Earth flat?” or “Can vaccines cause autism?”

TruthfulQA exposes a core problem: LLMs don’t yet “know” things; they predict words. If a false claim appears frequently online, the AI might repeat it, even if it “knows” better.

Some models, like GPT-4, use alignment techniques to filter out misinformation, but even they struggle with subtle falsehoods, like historical myths or conspiracy theories. The takeaway? A high-scoring AI here doesn’t mean it never lies, just that it lies less often.

HumanEval: Coding Benchmark

HumanEval tests whether an LLM can code, not just talk about coding. Rather than simple syntax tests, HumanEval requires logic, debugging, and writing fully functional programs.

It specifically presents 164 programming problems where the model must write working Python functions based on docstrings and function signatures. HumanEval is a favorite for evaluating models like GPT-4, Code Llama, and Claude.

At the time of this writing, Claude 3.5 Sonnet is the highest-ranking LLM for the HumanEval benchmark:

One quirk of HumanEval is the pass@k metric, which checks if a correct solution appears in multiple AI-generated attempts. This acknowledges a major LLM flaw: inconsistency. A model might get the answer right sometimes, but in real-world coding, “sometimes” isn’t good enough.

BIG-bench: General Intelligence Test

BIG-bench (Beyond the Imitation Game Benchmark) is essentially an IQ test for AI, packed with over 200 unique tasks designed to stretch an LLM’s reasoning, creativity, and problem-solving skills.

Unlike benchmarks that focus on specific areas like math or coding, BIG-bench throws everything at an AI (logical puzzles, ethical dilemmas, abstract reasoning, and even satirical humor detection). One task might ask a model to solve a tricky word problem, while another tests if it understands irony in a joke.

While BIG-bench is great at exposing LLM weaknesses, it’s still an academic test. A model doing well here doesn’t necessarily mean it’ll thrive in real-world applications, just like a high IQ score doesn’t guarantee street smarts.

GSM8K: Arithmetic Reasoning

GSM8K (Grade School Math 8K) is exactly what it sounds like. It consists of a dataset of 8,500 grade-school-level math word problems. Quite a few models today struggle with GSM8K and will often confidently deliver wrong answers because they predict what sounds right rather than actually solving the math.

GSM8K forces LLMs to break problems into steps, mimicking how humans work through equations. The best-performing models use chain-of-thought prompting, which encourages step-by-step explanations rather than instant answers.

At the time of this writing, Claude 3.5 Sonnet tops the GSM8K LLM benchmark leaderboard:

ARC: Science and Logic Benchmark

The AI2 Reasoning Challenge (ARC) is where AI meets scientific reasoning (and sometimes stumbles). ARC is designed to test a model’s ability to apply knowledge rather than just recall it. To test this, ARC contains grade-school-level science questions that require logical AI inference.

Early LLMs flunked ARC, often choosing answers that sounded correct but made zero logical sense. Newer models like GPT-4 and Claude do better, especially with chain-of-thought prompting, which forces them to explain their reasoning step by step.

But ARC is a great reminder that knowledge is useless without logic (and AI still struggles with that).

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Key Takeaways

LLM benchmarks tell us where AI language models shine and where they still stumble. Some test math and science, others push coding and reasoning, but all expose the gap between human and machine intelligence.

Three key insights to take with you:

• Just a few top LLM benchmarks to watch today include MMLU, HellaSwag, TruthfulQA, HumanEval, Big-bench, GSM8K, and ARC.
• No single benchmark captures an AI’s complete potential. Models excel differently across knowledge, coding, reasoning, and truthfulness tests.
• Benchmarks are proxies for capabilities we care about, not the capabilities themselves. LLMs often focus on narrow tasks, which can lead to models “gaming the system” by optimizing for high scores without improving real-world performance.

Companies like TensorWave are helping support benchmark-informed AI development with next-gen AI infrastructure. Connect with a Sales Engineer.