When a new model launches, you'll see the same scoreboards on the model card. MMLU. GSM8K. HumanEval. It honestly feels like alphabet soup at first. But once you know what each benchmark actually measures, you can read those tables with confidence and figure out what matters for your specific use case.

If you're new to this space, here's a primer on how large language models are built and evaluated.

Below is a plain-language guide to the benchmarks you keep hearing about. You'll see what each one tests, how it was built, and why big tech keeps highlighting it.

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1. General knowledge and reasoning

These benchmarks test broad world knowledge, basic logic, and commonsense reasoning across many subjects.

MMLU (Massive Multitask Language Understanding)

  • Tests: Multiple-choice questions across 57 subjects, from high school to professional level.

  • How it's built: Questions come from real exams, quizzes, and textbooks. The authors cleaned and curated them carefully.

  • Why it matters: This is the headline general knowledge score you'll see on most model cards. It's basically the SAT for AI models.

  • Learn more: https://en.wikipedia.org/wiki/MMLU

MMLU-Pro

  • Tests: A harder follow-up to MMLU with more challenging questions.

  • How it's built: It extends MMLU with new items and fixes known issues in the original dataset.

  • Why it matters: It separates top models that already max out classic MMLU. When everyone gets an A+, you need a harder test.

BIG-Bench and BIG-Bench Hard (BBH)

  • Tests: Hundreds of diverse tasks that probe reasoning, compositionality, and emergent abilities. BBH is a subset of 23 tasks that were especially difficult for earlier models.

  • How it's built: A large community crowdsourced tasks that stress reasoning and problem solving. Think of it as a collection of brain teasers.

  • Why it matters: It checks if a model can generalize across odd or synthetic tasks, not just memorize facts.

  • Learn more: https://arxiv.org/abs/2210.09261

ARC (AI2 Reasoning Challenge)

  • Tests: Multiple-choice science questions for grades 3 to 9. There are easy and challenge splits.

  • How it's built: Questions come from real US science exams. The challenge split requires reasoning beyond simple lookup.

  • Why it matters: It's a staple for measuring science knowledge plus basic reasoning. If a model can't handle middle school science, that tells you something.

  • Learn more: https://huggingface.co/datasets/allenai/ai2_arc

HellaSwag

  • Tests: Sentence completion. You choose the most plausible ending to a short scenario.

  • How it's built: Wrong endings are generated adversarially and filtered. This makes the options tricky even for strong models.

  • Why it matters: It measures commonsense understanding of everyday situations. Can the model predict what happens next in normal life?

  • Learn more: https://arxiv.org/abs/1905.07830

TruthfulQA

  • Tests: Whether the model avoids common misconceptions and urban legends across 38 topics.

  • How it's built: Humans wrote adversarial questions where people often answer incorrectly. The dataset includes truthful references.

  • Why it matters: It checks if the model repeats popular falsehoods. You'd be surprised how many models confidently state things that aren't true.

  • Learn more: https://arxiv.org/abs/2109.07958

GPQA (Graduate-level Google-Proof Q&A)

  • Tests: Very hard multiple-choice questions in physics, biology, and chemistry.

  • How it's built: Domain experts wrote questions that are difficult to answer by simple web search. You need deep reasoning, not just Google skills.

  • Why it matters: It probes expert-level knowledge, not trivia. This is PhD-level stuff.

  • Learn more: https://arxiv.org/abs/2311.12022

2. Math and logical reasoning

These benchmarks focus on multi-step math and symbolic reasoning. They expose gaps that don't show up on general knowledge tests.

GSM8K (Grade School Math 8K)

  • Tests: Word problems that usually take 2 to 8 steps and basic arithmetic.

  • How it's built: Experts wrote clean problems with a train and test split.

  • Why it matters: This is the core math reasoning metric that model cards often show. If a model can't do grade school math, that's a red flag.

  • Learn more: https://arxiv.org/abs/2110.14168

MGSM and MGSM-style multilingual sets

  • Tests: GSM8K-style problems translated into many languages.

  • How it's built: Careful translation of GSM8K into typologically diverse languages.

  • Why it matters: It shows if math reasoning holds up outside English. Math is universal, but can the model do it in French or Japanese?

GSM8K-Platinum

  • Tests: A tougher subset of GSM8K that exposes weaknesses in frontier models.

  • How it's built: The creators re-scored and filtered problems to increase difficulty.

  • Why it matters: It separates top-tier models that near-saturate GSM8K. When everyone aces the test, you need a harder one.

  • Learn more: https://gradientscience.org/gsm8k-platinum/

MATH

  • Tests: High school competition problems from AMC, AIME, and similar contests. Solutions are step by step in LaTeX.

  • How it's built: Contest problems were collected and annotated with full worked solutions across many topics.

  • Why it matters: It measures advanced multi-step reasoning beyond simple arithmetic. This is where things get serious.

  • Learn more: https://arxiv.org/abs/2103.03874

3. Coding and program synthesis

These benchmarks evaluate code generation with unit tests. Scores often report pass@k. That means the chance that at least one of k attempts passes all tests.

HumanEval

  • Tests: Short Python problems with a function signature and hidden tests.

  • How it's built: OpenAI created 164 tasks to evaluate code generation directly by running unit tests.

  • Why it matters: This is the most cited single coding metric on model cards. It's basically the standard.

  • Learn more: https://arxiv.org/abs/2107.03374

MBPP (Mostly Basic Python Problems)

  • Tests: Entry-level Python tasks with a natural-language description, a reference solution, and tests.

  • How it's built: Crowd-sourced problems that reflect beginner programming skills and common library use.

  • Why it matters: It checks basic coding competence and instruction following. Can the model write a simple function when asked?

  • Learn more: https://arxiv.org/abs/2108.07732

APPS

  • Tests: Thousands of problems from platforms like Codeforces and LeetCode, across difficulty levels.

  • How it's built: Scraped competitive programming and interview problems, with input and output tests for grading.

  • Why it matters: It evaluates problem solving at scale and at higher difficulty. This is where you separate the hobbyists from the pros.

  • Learn more: https://arxiv.org/abs/2108.07732

Tip: You'll often see HumanEval scores in the headline. MBPP and APPS sometimes appear in technical reports and blogs for extra context.

If you're planning a rollout, here are practical strategies for adopting GenAI tools in engineering teams.

4. Multimodal benchmarks

These tests combine text with images, diagrams, charts, or documents. They measure both visual understanding and language reasoning.

MMMU (Massive Multi-discipline Multimodal Understanding)

  • Tests: Image-text questions across 30 college subjects, including art, business, medicine, and engineering.

  • How it's built: Items come from college exams, quizzes, and textbooks. The images mix photographs, charts, and diagrams.

  • Why it matters: It's a broad and rigorous test for multimodal understanding. Can the model read a chart AND explain what it means?

  • Learn more: https://arxiv.org/abs/2311.16502

MathVista

  • Tests: Math in visual context. You solve problems that include graphs, geometric figures, and diagrams.

  • How it's built: The benchmark merges 28 existing datasets and adds new ones. It focuses on visual plus symbolic reasoning.

  • Why it matters: It's a key measure for models that claim visual math skills. Geometry isn't just about formulas.

  • Learn more: https://mathvista.github.io/

Classical VQA-style sets, such as VQAv2, TextVQA, DocVQA, and ChartQA

  • Tests: Question answering about images. Each variant targets a different domain, like natural photos, text inside images, scanned documents, or data charts.

  • How it's built: Crowd-sourced questions and answers on curated image collections.

  • Why it matters: Gemini and other multimodal reports rely on many of these to compare against GPT-4 and similar models.

5. Long-context and retrieval

These benchmarks stress memory and retrieval as context windows grow. And boy, are they growing.

Needle-in-a-Haystack tests

  • Tests: Whether a model can find a small needle sentence hidden in a very long context. This can be text, video frames, or audio transcripts.

  • How it's built: A known fact is inserted at a random position. The model must retrieve it.

  • Why it matters: Gemini 1.5 and similar long-context evaluations rely heavily on these setups. It's like asking someone to find one specific sentence in a 500-page book.

LongBench, RULER, and L-Eval

  • Tests: QA, summarization, and retrieval over long documents or many chunks.

  • How it's built: Existing QA and summarization datasets are scaled up. New synthetic long-document tasks are added.

  • Why it matters: It shows how performance degrades as inputs get longer. Most models get worse as the haystack gets bigger.

6. Safety, truthfulness, and robustness

These suites measure if the model stays safe, avoids toxic or biased content, and resists misinformation.

TruthfulQA

  • Purpose: Measures whether the model avoids common falsehoods and misconceptions. See section 1 for details.

Toxicity and bias sets, including RealToxicityPrompts and protected-class bias probes

  • Tests: Prompts designed to elicit toxic or biased outputs.

  • How it's built: The sets include scraped and manually screened toxic text and templates that touch on protected attributes.

  • Why it matters: Big model reports quote these in safety sections rather than in the main performance table. Nobody wants their model making headlines for the wrong reasons.

For a broader view of governance and ethics, see the principles of responsible AI and ethical considerations.

7. Standardized exams

These are real exams that researchers repurpose as test sets. They provide familiar human baselines.

Professional and academic exams, such as the bar exam, LSAT, SAT, GRE, and AP tests

  • Tests: Original or practice exam questions. Sometimes reformatted as multiple-choice QA.

  • Why it matters: Reports often claim human-level or top percentile performance on these. The framing is intuitive for readers. "It passed the bar exam" sounds impressive.

How big tech uses these scoreboards

You'll see a consistent pattern across model families. Here's what to look for.

For decision makers, here's guidance on aligning benchmark selection with your broader AI strategy.

OpenAI, for GPT-4, GPT-4.1, GPT-4o, and GPT-4o mini

Google DeepMind, for Gemini

  • Commonly reports: MMLU, BIG-Bench or BBH, ARC, GSM8K, MATH, HumanEval and MBPP, MMMU, MathVista, long-context needle tests, and many VQA or image benchmarks.

  • Learn more: https://arxiv.org/abs/2403.05530

xAI, for Grok

How to read these scores with confidence

  • Start with task fit. Ask yourself what you need. Do you need general knowledge, math reasoning, coding, multimodal input, or long-context retrieval?

  • Check difficulty tiers. Look for tougher variants such as MMLU-Pro, BBH, GSM8K-Platinum, and GPQA.

  • Look beyond a single number. Compare multiple related benchmarks to spot strengths and weaknesses. One great score doesn't tell the whole story.

  • Consider safety and truthfulness. Review TruthfulQA and toxicity or bias metrics for your risk profile.

  • Validate with your workload. If the stakes are high, run a small pilot that mirrors your real tasks. Benchmarks are useful, but your actual use case is what matters.

To connect scores to business value, explore frameworks for measuring the real-world impact and ROI of AI initiatives.

You now have a map of the benchmark landscape. The next time you see a model card, you can scan the table, match the benchmarks to your needs, and judge whether a reported gain will actually matter for your work. Because at the end of the day, a 2% improvement on MMLU might sound impressive, but if you just need the model to write emails, it probably doesn't matter much.