Large Language Model (LLM) benchmarking and evaluation refers to the practice of rigorously testing LLMs on standardized tasks to measure their capabilities, limitations, and alignment with desired behaviors. These benchmarks act like “exams” for AI models, providing a set of inputs/questions with ground-truth answers and a scoring methodology. By comparing model outputs against correct answers or human preferences, researchers can quantify how well an LLM performs on various skills – from basic language understanding to complex reasoning or coding. Benchmark results help identify strengths and weaknesses of models, guide improvements, and enable fair comparisons between different models.
Benchmarks are typically categorized by their purpose and focus area. Some are general-purpose or multi-domain evaluations spanning many tasks to give a holistic view of an LLM. Others are task-specific – targeting a particular capability such as coding, math, or commonsense reasoning. There are safety and alignment benchmarks designed to assess ethical and truthful behavior. Finally, some benchmarks emphasize multilingual performance, evaluating models across multiple languages. In this report, we focus on primarily English-language benchmarks (noting multilingual aspects where applicable).
At Hyperbolic, we use esults from many of the coding and math suites below to decide which open‑source models run best (and most cost‑effectively) on our GPU infrastructure—highlighting the real‑world importance of standardized “apples‑to‑apples” testing.
Knowledge & Reasoning Benchmarks
MMLU (Massive Multitask Language Understanding)
Description & Purpose: MMLU is a comprehensive benchmark introduced to assess an LLM’s breadth of world knowledge and problem-solving ability. It consists of 15,000+ multiple-choice questions across 57 subjects, ranging from elementary math and US history to computer science and law. Each question has four possible answers. The key idea is to evaluate models on a wide spectrum of academic and commonsense domains, at levels from high school up to expert difficulty. MMLU specifically tests models in a zero-shot or few-shot setting (no fine-tuning on the tasks), measuring how much knowledge and reasoning they have out-of-the-box. Performance is reported as accuracy (percentage of questions answered correctly), often broken down by subject and averaged overall.
Specific Focus: This benchmark is often used to check if a model’s pre-training has endowed it with factual and analytical knowledge across disciplines. Strong performance requires both recall of facts and reasoning to eliminate wrong options, making MMLU a general knowledge and reasoning test. It has been influential in tracking progress – early large models struggled on many MMLU subjects, whereas latest models show significantly higher averages, though gaps remain in advanced topics. An updated version called MMLU-Pro was proposed in 2023 with more difficult, professionally written questions and 10 answer choices for deeper challenge.
Open Source & References:
GPQA Diamond (Graduate-Level Reasoning)
Description & Purpose: GPQA Diamond is a subset of the GPQA dataset composed of 448 multiple-choice questions crafted by domain experts in biology, chemistry, and physics. It is intended to assess graduate-level reasoning capabilities, going beyond factual recall to test for deep conceptual understanding, abstraction, and multi-step scientific reasoning. The benchmark is designed to be “Google-proof,” requiring models to reason through content rather than retrieve memorized facts.
Specific Focus: The benchmark emphasizes rigorous question formats that mirror academic assessments, with distractors designed to challenge both retrieval-based and reasoning-based models. It is especially useful for evaluating models in STEM education and advanced knowledge applications.
Open Source & References
Paper: https://arxiv.org/abs/2311.12022
HellaSwag
Description & Purpose: HellaSwag, introduced by Zellers et al., is a benchmark designed to evaluate commonsense reasoning in language models. The task format is multiple-choice fill-in-the-blank: the model is given an incomplete description (a few sentences of a scenario) and must choose the most plausible ending from several options. What makes HellaSwag challenging is that the wrong answer options are adversarially generated – they are produced by models to be misleadingly plausible, so a model must rely on real commonsense understanding to pick the correct ending. This avoids trivial cues and forces the LLM to “understand” the context (e.g. physical and social commonsense) to continue the story correctly.
Specific Focus: HellaSwag specifically targets situational commonsense and next-sentence prediction beyond surface statistics. Even large models have historically found HellaSwag difficult, though progress has been made. It is a good test of whether a model can integrate everyday knowledge to infer what happens next in a given situation. It’s often grouped with benchmarks like Winogrande to measure commonsense reasoning.
Open Source & References
Open Source Repo: HellaSwag GitHub
Website: HellaSwag Dataset
ARC-AGI-1
(Abstraction and Reasoning Corpus for Artificial General Intelligence v1)
Description & Purpose: Introduced by François Chollet in 2019, ARC-AGI-1 is a benchmark designed to evaluate an AI system's general reasoning capabilities and its ability to generalize from limited data. The benchmark comprises 800 tasks, each consisting of a few input-output grid examples. The AI system must deduce the underlying transformation rule and apply it to new inputs to produce the correct outputs. This setup aims to assess an AI's fluid intelligence, mirroring human-like problem-solving abilities.
Specific Focus: ARC-AGI-1 emphasizes evaluating an AI system's capacity for generalization, abstraction, and reasoning. The benchmark presents tasks that require models to deduce transformation rules from minimal examples and apply them to novel inputs, simulating human-like problem-solving abilities. By restricting task-specific training and focusing on core knowledge priors, ARC-AGI-1 ensures that success reflects intrinsic reasoning capabilities rather than memorization or pattern recognition. This design aims to measure fluid intelligence, assessing how efficiently a system can acquire new skills and adapt to unfamiliar situations.
Open Source & References
Open Source Repo: ARC-AGI GitHub
Paper: On the Measure of Intelligence
Math & Problem-Solving Benchmarks
GSM8K (Grade School Math 8K)
Description & Purpose: GSM8K is a benchmark of grade-school level math word problems. It contains 8,500 problems that typically require 2 to 8 steps of reasoning to solve. Each problem is given in natural language (English), often involving basic arithmetic or simple algebra framed in everyday scenarios, and the model must produce the correct numerical answer. The focus is on testing an LLM’s multi-step reasoning ability: a successful model needs to parse the question, possibly perform calculations or logical steps in sequence, and arrive at a final answer. Simply having math facts is not enough – the model must simulate a chain-of-thought process.
Specific Focus: GSM8K helped popularize the use of prompting methods like chain-of-thought, where the model is encouraged to show its intermediate reasoning. Even large models often struggle if they try to answer directly but perform better when allowed to work through the steps. GSM8K remains a challenging benchmark for many models: while top-tier LLMs now solve a majority of problems, smaller models frequently make mistakes on the multi-step logic required. It specifically evaluates factual correctness in mathematical reasoning, a capability critical for domains like finance or engineering.
Open Source & References
Open Source Repo: GSM8K GitHub
Website: GSM8K on Hugging Face
MATH
Description & Purpose: Introduced by Hendrycks et al. in 2021, the MATH dataset is designed to evaluate the mathematical reasoning capabilities of AI models. It comprises 12,500 challenging competition-level mathematics problems, each accompanied by detailed step-by-step solutions. The problems are sourced from high school mathematics competitions, including AMC 10, AMC 12, and AIME, covering a wide range of topics and difficulty levels. This dataset aims to assess an AI model's ability to perform complex mathematical reasoning and problem-solving tasks.
Specific Focus: MATH emphasizes a wide range of mathematical domains, including algebra, geometry, number theory, counting and probability, prealgebra, intermediate algebra, and precalculus. Each problem is rated on a difficulty scale from 1 to 5, enabling fine-grained assessment of model performance across varying complexities. The inclusion of detailed step-by-step solutions provides valuable resources for training models to generate derivations and explanations, supporting the development of systems capable of deep mathematical reasoning. Despite advances in large language models, achieving high accuracy on such complex problems remains challenging; the benchmark highlights that simply scaling model size and compute may not be sufficient—novel algorithmic approaches are likely required to improve mathematical reasoning in AI.
Open Source & References
Open Source Repo: MATH GitHub
Dataset on Hugging Face: Hendrycks MATH Dataset
Paper: Measuring Mathematical Problem Solving With the MATH Dataset
MATH 500 (Math Problem-Solving)
MATH 500 is a subset of the MATH dataset curated to include 500 challenging competition-style problems. These represent high-difficulty tasks often used for evaluating models like GPT-4.1 in academic-grade mathematical problem-solving. The benchmark demands symbolic manipulation, precise multi-step reasoning, and explanation of intermediate steps. It provides a condensed but high-signal measure of math proficiency.
AIME 2024 (High School Math Competition)
Description & Purpose: Based on the American Invitational Mathematics Examination, AIME 2024 questions assess LLMs on advanced pre-college mathematical problem-solving. These are often used in elite math competitions in the U.S.
Specific Focus: The benchmark includes 15 integer-answer questions requiring deep insight, algebraic manipulation, and deductive reasoning. It is a good proxy for evaluating symbolic generalization.
Coding Benchmarks
SWE-bench Verified (Agentic Coding)
Description & Purpose: SWE-bench Verified is a benchmark released by OpenAI that measures an LLM’s ability to resolve real GitHub issues in open-source software repositories. Unlike traditional coding benchmarks, this benchmark evaluates agentic behaviors—autonomously navigating, editing, and fixing codebases.
Specific Focus: Each task includes a natural-language issue, relevant source code, and tests that must pass. SWE-bench Verified is a filtered subset where human evaluators confirmed task validity and correctness, enabling a reliable test of practical software engineering competence in LLMs.
Open Source & References:
HumanEval
Description & Purpose: HumanEval is a benchmark created by OpenAI to assess the code generation ability of LLMs. It consists of 164 programming problems written as Python function specifications: each includes a docstring describing the task, a function signature to complete, and several unit tests. The challenge for the model is to generate the function body such that all the unit tests pass. HumanEval focuses on functional correctness rather than just syntactic code generation. The primary metric is pass@k, which measures the probability that at least one of the model’s top k generated solutions passes all tests.
Specific Focus: This benchmark mirrors how human developers validate code – by running tests. It incentivizes models that not only write code that “looks” correct, but actually executes correctly. HumanEval is now a standard for evaluating code-focused models like Codex and other code-capable LLMs. High performance on HumanEval indicates an LLM can understand a problem description and produce logically correct, runnable code.
Open Source & References
Open Source Repo: HumanEval GitHub
BigCodeBench
Description & Purpose: BigCodeBench is a next-generation coding benchmark introduced in 2024 by the BigCode community. It dramatically expands on HumanEval by featuring 1,140 coding tasks across 7 domains, requiring the use of 139 different programming libraries. Each task is a realistic coding scenario where the model must compose multiple API or library calls to solve a problem – for example, performing data analysis with pandas, or creating a plot with matplotlib. This tests a model’s ability to use tools and libraries as “plugins” in code.
Specific Focus: BigCodeBench provides two modes: Complete (generate code from a detailed docstring) and Instruct (generate code from a concise natural language instruction). Every generated solution is checked against multiple unit tests, making evaluation rigorous. By covering many libraries and requiring multi-step API usage, BigCodeBench evaluates practical programming proficiency – not just solving algorithmic puzzles, but building correct solutions in real-world contexts. Initial results showed that even the best models achieve around 60% accuracy, indicating ample room for improvement. BigCodeBench has quickly become a new gold standard for evaluating code generation in LLMs.
Open Source & References
Open Source Repo: BigCodeBench GitHub
Website: BigCodeBench Leaderboard
Safety & Alignment Benchmark
TruthfulQA
Description & Purpose: TruthfulQA is a benchmark focused on truthfulness and misinformation avoidance in LLMs. It consists of 817 questions spanning 38 categories that probe for common misconceptions, falsehoods, or misleading queries. The questions are things that people often answer incorrectly (e.g., conspiracy theories, myths, or tricky knowledge questions). The task for the model is to produce a truthful answer rather than echo popular false beliefs. For example, a question might be “Can vaccines cause autism?” – a truthful answer would explain that scientific consensus says no, whereas an untruthful model might say “Yes” if it has picked up that misconception. Answers are evaluated on truthfulness and also informativeness.
Specific Focus: This benchmark tests an alignment aspect: does the model avoid “hallucinating” false facts or repeating misconceptions? It’s a direct measure of how well an LLM’s knowledge and calibration align with factual reality, especially on topics prone to myth. Many powerful LLMs struggle with TruthfulQA – models often mimic human falsehoods or confidently generate incorrect answers, especially if the question is phrased in a leading way. TruthfulQA has become a standard evaluation for the safety/alignment dimension of LLM performance, alongside other tests for bias or toxicity.
Open Source & References
Open Source Repo: TruthfulQA GitHub
TruthfulQA on Papers with Code
Leaderboards and Interactive Evaluations
Hugging Face Open LLM Leaderboard
Description & Purpose: The Open LLM Leaderboard is an automated evaluation platform hosted by Hugging Face that tracks and ranks publicly available LLMs on a standard set of benchmarks. Launched in 2023, it provides a centralized leaderboard where any open-source model (and some closed models via API) can be evaluated under the same conditions. The purpose is to highlight state-of-the-art models in the open AI community and allow developers to make data-driven comparisons. The leaderboard runs each model through a battery of six benchmark tasks that cover distinct aspects of performance. These tasks include: IFEval (instruction following accuracy), BBH (Big-Bench Hard) for complex reasoning, MATH (level 5) for advanced math, GPQA (graduate-level closed-book QA), MuSR (multi-step soft reasoning puzzles), and MMLU-PRO (an enhanced professional version of MMLU).
Specific Focus: The Open LLM Leaderboard is updated continuously as new models are submitted, enabling a live comparison of models. It emphasizes tougher benchmarks and fair scoring: the inclusion of BBH and MMLU-Pro reflects an attempt to go beyond benchmarks that many models have already mastered. It has become a popular reference; for instance, developers choosing an open-source model often consult this leaderboard to see how models stack up on standardized tests of reasoning, math, and knowledge.
Open Source & References
Chatbot Arena (LMSYS)
Description & Purpose: Chatbot Arena is an interactive evaluation platform introduced by LMSYS for comparing LLM-based chatbots via human pairwise preference. Instead of automated quiz-style questions, Chatbot Arena lets two models have a side-by-side conversation with a user prompt, and human evaluators (or crowd users) vote on which model’s response is better. Through many such battles, an Elo rating is computed, producing a ranking of chatbots by human preference. This approach tests models on open-ended conversation quality, helpfulness, and overall user experience.
Specific Focus: Unlike objective benchmarks with right-or-wrong answers, Chatbot Arena is subjective. It captures human judgments on qualities like coherence, usefulness, harmlessness, and conversational flow. This makes it a strong indicator of real-world user satisfaction – arguably the “end-user experience” of a model. To complement this, LMSYS also introduced MT-Bench (Multi-Turn Benchmark): a fixed set of 80 chat questions where model responses are rated by GPT-4 as a proxy for human evaluation. Together, Chatbot Arena and MT-Bench have become widely used for evaluating chat assistant quality.
Open Source & References
Other Benchmarks
TAU-bench (Agentic Tool Use)
Description & Purpose: TAU-bench is a benchmark evaluating agentic tool use—how well LLM agents interact with external tools or simulated users to perform real-world tasks. It is designed to assess AI capabilities in executing workflows that require iterative tool invocation, adaptive planning, and external information synthesis.
Specific Focus: The benchmark contains multi-stage tasks across knowledge-based queries, structured planning, and tool chaining. It reflects scenarios like scheduling, booking, and software automation where agent autonomy is crucial.
Open Source & References
https://arxiv.org/abs/2406.12045
MMMU Validation (Visual Reasoning)
Description & Purpose: MMMU (Massive Multi-discipline Multimodal Understanding) validation set evaluates multimodal models on visual reasoning using college-level exam questions. The benchmark spans diverse fields, such as medicine, engineering, and design, requiring visual-textual comprehension.
Specific Focus: Tasks include diagrams, schematics, or image-supported questions alongside text. It challenges models to perform multi-step logic, understand visuals in context, and provide structured reasoning, making it ideal for testing LLMs with image-processing capabilities.
Open Source & References
https://mmmu-benchmark.github.io/
General-Purpose Evaluation Suites
Stanford HELM (Holistic Evaluation of Language Models)
Description & Purpose: HELM is a comprehensive evaluation framework that assesses LLMs across a broad range of scenarios and metrics. Developed as a “living benchmark” by Stanford in 2022, HELM emphasizes transparency and multi-dimensional analysis. Unlike single-metric benchmarks that only measure accuracy, HELM evaluates seven metrics (e.g. accuracy, calibration, robustness, fairness, bias, toxicity, efficiency) over many use-case scenarios. This holistic approach ensures that models are tested not just on their core NLP tasks (like question answering, summarization, etc.), but also on robustness to adversarial inputs and bias/fairness outcomes. By covering 16+ core scenarios and multiple metrics, HELM provides a “complete picture” of an LLM’s capabilities and risks, highlighting areas for improvement (e.g. where a model might be accurate but not calibrated or fair).
Specific Focus: HELM’s scenarios are grouped into core (standard NLP tasks such as QA, translation, summarization), stress tests (adversarial or challenging inputs), and targeted fairness/bias tests. This yields rich insights into model behavior on accuracy vs. toxicity trade-offs, bias in outputs, and robustness to distribution shifts. HELM’s multi-metric focus has made it a foundational benchmark for responsible AI evaluation. It has a public leaderboard and is updated as a “living” benchmark to track progress over time.
Open Source & References:
Open Source Repo: HELM GitHub
Website: HELM Benchmark
BIG-bench (Beyond the Imitation Game Benchmark)
Description & Purpose: BIG-bench is a large-scale, collaborative benchmark released in 2022 to test the limits of LLM capabilities. It comprises over 200 diverse tasks contributed by the research community, covering topics from linguistics and commonsense reasoning to math, science, and beyond. The goal of BIG-bench is to evaluate whether models can go beyond pattern recognition and approach human-level reasoning and understanding across a wide array of challenges. Many tasks are unconventional or creative, designed specifically to be difficult for machines – for example, logic puzzles, metaphor interpretation, or deliberate adversarial questions. This broad coverage makes BIG-bench a general-purpose stress test for advanced LLMs.
Specific Focus: Each BIG-bench task has its own input-output format and metric (multiple-choice, free-form generation scored by humans or heuristics, etc.), and results are often aggregated to see where an LLM excels or fails. A subset of especially challenging tasks, known as BIG-bench Hard (BBH), was identified to focus on areas where even strong models struggle. BBH includes 23 tasks that probe complex multi-step reasoning (e.g. logical deduction, code generation challenges) and correlates well with human evaluation of model quality. Overall, BIG-bench provides a stress-testing suite covering everything from basic knowledge to advanced reasoning, often highlighting capability gaps that do not appear in simpler benchmarks.
Open Source & References
Open Source Repo: BIG-bench GitHub
References
LLM Benchmarks Explained: Everything on MMLU, HellaSwag, BBH, and Beyond – Confident AI
20 LLM Evaluation Benchmarks and How They Work – Zeta Alpha
https://zeta-alpha.com/blog/20-llm-evaluation-benchmarks-and-how-they-work
What Are LLM Benchmarks? – IBM
Top 10 LLM Benchmarking Evals. – Himanshu Bamoria, Medium
https://medium.com/@himanshu_72022/top-10-llm-benchmarking-evals-c52f5cb41334
Holistic Evaluation of Language Models (HELM) – Stanford CRFM
[2211.09110] Holistic Evaluation of Language Models – arXiv
BIG-bench (Beyond the Imitation Game Benchmark) – Google Research
MMLU: Measuring Massive Multitask Language Understanding – arXiv (Hendrycks et al., 2021)
HellaSwag: Can a Machine Really Finish Your Sentence? – Zellers et al., 2019
GSM8K: Training Verifiers to Solve Math Word Problems – Cobbe et al., 2021
HumanEval: Evaluating Large Language Models Trained on Code – OpenAI (Chen et al., 2021)
BigCodeBench – BigCode Project, ICLR 2025 Submission
TruthfulQA: Measuring How Models Mimic Human Falsehoods – Lin et al., 2021
Open LLM Leaderboard – Hugging Face
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
Chatbot Arena – LMSYS, Vicuna Team
ARC-AGI (Abstraction and Reasoning Corpus for AGI) – François Chollet
MATH Dataset: Measuring Mathematical Problem Solving With the MATH Dataset – Hendrycks et al., NeurIPS 2021
SafetyBench: Evaluating the Safety of LLMs – arXiv, 2023
MT-Bench: Evaluating LLMs in Multi-Turn Dialogues – LMSYS
https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/mt-bench.md
Which LLM Suits You? Optimizing the use of LLM Benchmarks Internally – RiskInsight
About Dr. Jasper Zhang, PhD
Dr. Jasper Zhang is the CEO and Co-founder of Hyperbolic. A mathematical prodigy, he completed his Ph.D. in Mathematics at UC Berkeley in just two years. He is a Gold Medalist in both the Alibaba Global Math Competition and the Chinese Mathematical Olympiad. Before founding Hyperbolic, he held roles at Ava Labs and Citadel Securities, bringing deep expertise in quantitative finance and AI.
Connect with Jasper on X and Linkedin.
About Hyperbolic
Hyperbolic is the on-demand AI cloud made for developers. We provide fast, affordable access to compute, inference, and AI services. Over 195,000 developers use Hyperbolic to train, fine-tune, and deploy models at scale.
Our platform has quickly become a favorite among AI researchers, including those like Andrej Karpathy. We collaborate with teams at Hugging Face, Vercel, Quora, Chatbot Arena, LMSYS, OpenRouter, Black Forest Labs, Stanford, Berkeley, and beyond.
Founded by AI researchers from UC Berkeley and the University of Washington, Hyperbolic is built for the next wave of AI innovation—open, accessible, and developer-first.
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