How to Evaluate State‑of‑the‑Art LLM Models: A Complete Benchmarking Guide

Although the origins of Large Language Models (LLMs) date back to the late 1990s, it wasn’t until the launch of ChatGPT in 2022 that they gained mainstream attention. Since then, new models such as GPT-4, Gemini, and Grok have rapidly emerged, each claiming to outperform the previous state-of-the-art (SOTA).

But what does it mean for one model to “outperform” another? And how can you determine whether a new LLM is genuinely better for your specific needs? The answer lies in systematic evaluation using standardized benchmarks-comprehensive test suites designed to assess an LLM’s foundational abilities, including language understanding, reasoning, and programming.

In this article, we will explore the process of evaluating SOTA LLMs against benchmarks by examining the most recent benchmarks, identifying critical metrics for comparison, and outlining best practices for running your evaluations.

Benchmarking tests with rigorously designed, standardized evaluations so that their performance on a wide range of tasks can be quantified and compared objectively. These assessments cover every critical capability that determines an LLM’s practical value.

• Language understanding gauges how well a model comprehends and generates human-like text for activities such as summarization, translation, and conversation, which are key for customer support and content creation.
• Reasoning measures the model’s ability to apply logical inference and tackle multi-step problems, which is vital in science-focused domains like biology or physics.
• Coding benchmarks assess whether the model can generate correct, functional code in languages such as Python or Java.
• Mathematical problem-solving tests range from basic arithmetic to advanced calculus.

Because a model might excel at, say, code generation yet lag in reasoning or multilingual support, benchmarking highlights each model’s strengths and weaknesses, reveals trade-offs, tracks progress over time, and ultimately guides you toward the model best suited to your real-world needs.

Some of the most notable LLM benchmarks for evaluating different aspects of a model’s capabilities are outlined below.

These benchmarks test an LLM’s ability to apply logic and everyday knowledge to solve problems.

1. HellaSwag-Pro

HellaSwag-Pro is a bilingual benchmark that overcomes the limitations of English-only tests and checks, verifying whether models genuinely reason rather than rely on memorized patterns. It offers 11,200 human-verified question variants in both English and Chinese, grouped into seven variant types such as negative phrasing and restatements (see Figure 1). By rewording questions, these variants probe whether a model can still select the correct answer, thereby measuring its reasoning ability grounded in language comprehension.

Figure 1: Examples of the seven variants adopted for an initial question, with the correct answer unchanged as (A). Modifications are highlighted in different colors for clarity.
(Source)

Evaluations across 41 open- and closed-source LLMs show that questions with negative phrasing are the hardest, while restated problems are relatively straightforward. GPT-4o proved most resilient, achieving 69% accuracy and sustaining steady performance across all variant types.

2. MultiChallenge

MultiChallenge is a benchmark designed to evaluate LLMs on multi-turn conversations, a critical but underexplored area for real-world applications. It focuses on four realistic and commonly observed challenges in human-LLM interactions: instruction retention, user information recall, reliable versioned editing, and self-coherence. These tasks require models to follow instructions, manage context across turns, and reason effectively in dialogue settings.

Figure 2: Sample conversations in MultiChallenge for the four categories (Source)

Figure 2 illustrates that each MultiChallenge test case contains up to ten dialogue turns and concludes with a user query. To answer correctly, a model must draw on the entire exchange, demonstrating its ability to integrate and reason over prior context.

Despite strong showings on other multi-turn benchmarks, cutting-edge models struggle with MultiChallenge. Early evaluations placed every state-of-the-art system, including GPT-4o, Llama 3 1405B Instruct, and Gemini 1.5 Pro below the 50% mark, with Claude 3.5 Sonnet (June 2024) leading at 41.4% accuracy. Public results as of July 2025 still top out at 41.4% accuracy (Claude 3.5 Sonnet, June 2024); no peer-reviewed score for O3 Pro has been released yet.

3. Humanity’s Last Exam (HLE)

Humanity’s Last Exam is a multimodal benchmark crafted to gauge expert-level mastery in more than 100 disciplines, including mathematics, physics, computer science, the humanities, and the natural sciences.

Figure 3 shows samples of questions included in the dataset. It consists of 2,500 multiple-choice questions (where the model selects one of five or more answer choices) and exact-match questions (where the model provides an exact string as output), suitable for automated grading. To ensure quality and difficulty, and eliminate questions that rely on simple internet retrieval, each item is pre-screened using SOTA models such as GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet, and O1. Questions that are answered correctly by these models are removed. The remaining questions undergo a two-stage review process, first by graduate-level reviewers, then by expert validators.

Figure 3: Samples of the diverse and challenging questions submitted to HLE. (Source)

The initial performance results, shown in Figure 4, indicate that even the strongest current LLMs perform poorly on HLE, with accuracy remaining consistently low across the board. While these low scores are partially due to the benchmark’s question selection process, the results confirm the significant gap between current LLM performance and expert-level reasoning on closed-ended academic questions.

Figure 4: Accuracy of LLMs on HLE (Source)

The latest updates indicate that the Gemini 2.5 Pro preview achieves the highest performance in this benchmark, with an accuracy of approximately 21.64%.

These are concerned with the mathematical problem-solving ability of a model, from elementary arithmetic to advanced topics, including calculus.

1. U-MATH

U-MATH comprises 1,100 unpublished, open-ended university-level math problems drawn directly from real course materials. It spans six core areas: precalculus, algebra, differential calculus, integral calculus, multivariable calculus, and sequences & series. Approximately 20% of the items are multimodal, requiring both textual and visual comprehension. Designed to mirror the complex, multi-step reasoning typical of college assignments (see Figure 5), the benchmark highlights model performance gaps: among text-only systems, Qwen 2.5-Math-72B tops the chart at 50.2% accuracy, while in the multimodal class, Gemini 1.5-Pro-002 leads with 60.1%.

Figure 5: Sample question in the U-MATH benchmark. (Source)

2. CHAMP (Concept and Hint-Annotated Math Problems)

CHAMP is a collection of 270 high school math competition problems, each annotated with broad mathematical concepts and specific hints, as shown in Figure 6. These annotations allow controlled experiments on how LLMs use side information, whether helpful, misleading, or irrelevant, to solve complex problems. CHAMP is specifically designed to evaluate multi-step reasoning and solution verification abilities in LLMs.

Figure 6: A sample from the CHAMP dataset, which shows the problem (top), the concepts and hints (middle), and the complete solution (bottom). (Source)

The evaluation of a range of proprietary and open-source LLMs, including GPT-3.5/4/4 Turbo, PaLM 2 Medium, LLaMA 2/3 (7B-70B), Mistral 7B, and Mixtral 8x22B, shows that most models struggle with verifying solutions or following the verification prompt. Analysis also shows that models often arrive at correct answers through flawed reasoning, revealing gaps in their internal logic and verification capabilities. GPT-4 Turbo emerged as the best model, achieving a specificity of 77.8% and a sensitivity of 33.8% for problems with concepts and hints provided.

Coding benchmarks assess an LLM’s real-world programming competence, its ability to understand multi-step instructions, generate correct code, and pass hidden test cases across diverse libraries.

1. SWE-Bench Multimodal

SWE-Bench Multimodal (SWE-bench M) is a benchmark created to test how well AI systems generalize to visual, user-facing software environments. It targets bug-fixing tasks in JavaScript libraries commonly used for UI design, data visualization, and interactive web applications. The dataset comprises 617 tasks derived from 17 libraries, each containing at least one visual element, diagrams, screenshots, or visual unit tests within the task description or test cases (see Figure 7 for examples).

Figure 7: Four tasks from SWE-bench Multimodal. (Source)

Model performance on SWE-bench M depends heavily on the kind of visual input provided. Code snippets, diagrams, and UI screenshots each demand distinct reasoning skills, stretching the limits of multimodal understanding. Further complicating matters, JavaScript combines object-oriented, functional, and procedural paradigms, resulting in varied code structures that make it challenging for models to rely on standard bug-fixing strategies.

GPT-4o and Claude 3.5 Sonnet were evaluated using the benchmark on five systems: Retrieval-Augmented Generation (RAG), Agentless JS, and three SWE-agent variants (Base, JS, and M). While overall performance remained low, SWE-agent systems resolved 11.5% of tasks on average compared to 5.5% (RAG) and 3.9% (Agentless JS). The authors demonstrate that neither JavaScript-specific customizations (in SWE-agent JS and M) nor switching between GPT-4o and Claude 3.5 Sonnet resulted in meaningful performance differences.

2. BigCodeBench

BigCodeBench is a benchmark designed to evaluate how well LLMs can solve practical, complex programming tasks using multiple function calls across 139 libraries and seven domains. It includes 1,140 fine-grained Python tasks, each accompanied by an average of 5.6 test cases and achieving 99% branch coverage, ensuring rigorous evaluation.

The benchmark covers two common scenarios:

• BigCodeBench-Complete: code completion from structured docstrings.
• BigCodeBench-Instruct: code generation from natural-language instructions.

Figure 8: Example task in BigCodeBench (Source)

Figure 8 shows that BigCodeBench programming tasks embed expert-annotated docstrings filled with intricate instructions. Candidate solutions run in a fully configured environment and are judged against a rigorous suite of test cases.

Testing 60 LLMs reveals persistent difficulties: models often struggle with multi-step function calls and complex directives. GPT-4o led the pack but still solved only 60% of BigCodeBench-Complete tasks and less than 50% on BigCodeBench-Instruct, well short of the 97% human baseline. Models such as GPT-4 also tended to decline or lose track of lengthy instructions, resulting in additional failures.

Understanding the benchmarks set for a given model is equally important as identifying the metrics that matter.

Figure 9: Evaluation metrics for LLMs
Source: Author

• Accuracy measures the ratio of outputs measured against known truths. It captures a percentage of the outputs considered correct and is most useful in classification and multiple-choice question answering (QA) tasks. This is less meaningful for open-ended generation tasks where subjectivity plays a significant role.
• Precision evaluates how many of the factual claims made are true. This metric is significant in retrieval tasks, information verification, and any other context where false positives are undesirable.
• Recall evaluates how many of the pertinent facts are collected. It also focuses on inclusion when completeness is of utmost importance, for example, in multi-step question answering (QA) or document summarization.
• F1 Score is designed for an equally balanced count of adding and removing incorrect facts. It represents the mean of precision and recall.
• Perplexity is primarily focused on the training phase of the model, particularly when comparing the quality of base models. It is also used to evaluate model outputs, measuring its ability to predict upcoming words. Outputs with low perplexity are more confident and coherent.
• BLEU and ROUGE focus on word intersections to evaluate the generated text against the reference texts. BLEU and ROUGE are especially useful when there is a clear reference text (ground truth) and exact word choice matters, such as in machine translation or extractive summarization benchmarks.
  • BLEU is primarily used in translation as it provides precision-based evaluations.
  • ROUGE is recall-based and common in summarization tasks.

BERTScore and MoverScore utilize contextual embeddings to assess semantic similarity in relation to a reference text. As metrics, they are more meaningful than word overlap, as they are functional for paraphrasing and abstraction.

Evaluating LLMs typically involves either human judgment or automated scoring, each suited for different goals.

Human Evaluation

Human evaluation is most effective for evaluating subjective aspects such as usefulness, clarity of information, and tone. A prominent example is LMArena, developed by LMSYS, where users judge from two anonymous LLMs and vote for the better one. It employs an Elo rating system, like those used in chess, to rank models based on user preferences. As of July 2025, Gemini 2.5 Pro is #1 with an overall Elo of 1,463 and also tops every sub-category. For example, it scores 1,386 in the WebDev coding arena.

Figure 10: Current top 10 LLMs in LMArena (Source)

Use human evaluation when your focus is on more detailed feedback or user choice. It works best for long-form outputs but is slower, more complicated to scale, and more expensive.

Automated Evaluation

Automatic evaluation techniques are fast and highly scalable, making them ideal for environments where consistency and throughput are essential. Such techniques have evolved significantly over time, with various methodologies suitable for different evaluation requirements.

Traditional metrics, such as BLEU, ROUGE, and METEOR, rate generated text by counting token overlap with reference material. This approach works well when surface-level similarity strongly signals quality, such as in translation or summarization, but it misses finer points, including semantic accuracy and conceptual flow.

To fill that gap, model-based evaluators have emerged. Tools such as BLEURT and NLI-based scorers assess factual consistency and semantic integrity, rather than relying solely on raw overlap. They outperform the classic statistics on most tasks, yet their reliability wanes for longer or more complex outputs, where nuance and broader context become critical.

Most recently, LLMs themselves have been leveraged as evaluators, a paradigm known as LLM-as-a-judge. These approaches provide evaluators with a flexible, human-centric framework that can adapt to varied assessment criteria. Two leading techniques, G-Eval and DAG, offer complementary strengths:

• G-Eval scores outputs against predefined qualities such as coherence and style, making it ideal for subjective, task-specific metrics.
• DAGs (decision audit graphs) verify whether outputs satisfy a sequence of rule-based conditions, which is valuable for structured or compliance-driven evaluations.

Figure 11: Traditional metrics-based evaluation vs. LLM-as-a-judge approach
Source: Author

These advancements collectively illustrate the direction for more sophisticated and contextually aware evaluation strategies, with LLM-based strategies extending the frontier for automated grading.

Benchmarking SOTA LLMs isn’t always straightforward due to the following reasons:

1. Benchmark Saturation: A significant number of the SOTA models currently achieve over 90% accuracy on well-known benchmarks, such as MMLU and MATH, making it increasingly challenging to differentiate them. Therefore, it is necessary to evaluate model performance across newer, more challenging benchmarks.
2. Prompt Sensitivity: Minor changes in phrasing or the structure of prompts cause drastic performance shifts. This makes reproducibility difficult unless prompts are standardized and made widely available.
3. Hidden Fine-Tuning: Some LLMs may be fine-tuned using publicly available datasets, which can lead to overestimation of their performance, while underperforming on novel, real-world tasks.
4. Reproducibility & Openness: Not all benchmarks are open-source or well-documented. Closed tests are not transparent, especially in the case of vendor-published leaderboards.

If you’re building an internal benchmarking pipeline, follow these steps for clarity and consistency.

Figure 12: Best practices when benchmarking LLMs
Source: Author

1. Identify Your Goals in Advance: Whether you require factual accuracy, quality of generations, programming ability, or multi-turn reasoning, your goal will determine the appropriate benchmark and metric.
2. Employ Multiple Benchmarks: A single benchmark is not enough to evaluate the multifaceted capabilities of a model. Employ general and task-related benchmarks in combination to gain an overall perspective of model generalizability.
3. Standardize Prompts: Use the same prompt templates for all models to ensure balanced comparison. Avoid prompt leaks (e.g., reusing test questions in fine-tuning).
4. Isolate Variables: The external variables, such as context, token limit, and stop sequence, must remain constant while comparing the models.
5. Include Human Review: At a minimum, a small-scale human review process should be incorporated into customer-facing applications, even if the automated measures appear satisfactory.
6. Visualizing the Results: Scorecards and charts help stakeholders see the model’s performance at a glance.
7. Evaluate Regularly: Benchmarks should not be set once and then forgotten. Run them whenever you modify a model, a new competitor or benchmark emerges, or you change your task specifications.
8. Document Thoroughly: Version your benchmark datasets and prompts and store logs of model outputs, settings, and scores. This allows re-running tests later and comparing models fairly over time.

In a world where LLMs are evolving at a lightning pace, benchmarking is not an option; it’s a must. The right benchmarking strategy will save you time, reduce risk, and enable you to select the optimal model for your practical application, rather than the one with the highest paper score.

As the model advances, so must your approach to assessment. Keep testing, keep comparing, and keep validating.