One of the most common questions I get asked by senior managers during training and consulting sessions on Generative AI is “how do I choose an appropriate LLM for my use case?”.

Many LLM model websites share benchmarks comparing their performance with other models. In this guide, we will explore the common evaluation benchmarks, and how can you use them to determine whether a specific LLM is a good fit for your use case.

Common Evaluation Benchmarks for Large Language Models

At the heart of LLM comparison are specific benchmarks—standardized tests designed to evaluate various capabilities of a model. These tests are broken down into different subject areas like general knowledge, reasoning, math, multilingual capabilities etc. By looking at these scores, we can asses whether a model is good at certain types of tasks.

Most open use LLMs such as Llama and others share their evaluation results online. This makes it easy for us to compare them.

For example, here are the benchmarks for Llama 3.2 LLM shared at llama.com:

At a high level, these benchmarks are broken down into 6 categories. Below is a quick summary of the 6 categories.

General Language Understanding

MMLU (Massive Multitask Language Understanding) – 5-shot

This benchmark evaluates the model's performance across a variety of academic subjects, like history, math, and science. The "5-shot" part means that the model gets five examples to learn from before it's tested. This is critical for evaluating how well a model can generalize from a few examples - a key skill in real-world tasks, especially when training datasets are limited.

Open-rewrite eval (0-shot, rougeL)

Here, the model is tested on its ability to rewrite text without any prior examples (hence known as "0-shot"). RougeL is a metric that measures how well a model rewrites text that aligns some reference text. This is often used in summarization or paraphrasing tasks.

TLDR9+ (test, 1-shot, rougeL)

Similar to the open-rewrite evaluation, TLDR9+ focuses on summarizing text, but with one example provided. This gives you a better idea of how well the model can compress information into concise summaries.

IFEval

This is a test that measures the model’s ability to understand and generate formal text, such as legal documents or technical manuals. It’s a useful test to evaluate y the ability of a model to navigate niche, structured types of content.

Mathematical Capabilities

GSM8K (0-shot, CoT)

GSM8K evaluates the model’s ability to solve grade-school level math problems. The "0-shot" indicates that the model is tested without prior examples, and "CoT" stands for Chain of Thought - an evaluation technique that checks how well the model can reason through problems step-by-step. This is essential if you're planning to use an LLM in tasks requiring complex problem-solving.

MATH (0-shot, CoT)

Similar to GSM8K, but focused on more advanced math problems. Again, the emphasis is on step-by-step reasoning (using COT), which is valuable for scientific and engineering applications.

Reasoning and Problem Solving

ARC Challenge (0-shot)

The ARC Challenge is a reasoning test that tests the model’s ability to solve multiple-choice questions requiring logical thinking. Zero-shot performance is important here, as it indicates how well the model can handle questions without any prior examples, making it a good benchmark for assessing problem-solving capabilities in unfamiliar situations.

GPQA (0-shot)

This benchmark evaluates the model’s general-purpose question answering ability. It measures how well the LLM can handle various topics without specialized training on a specific domain.

Hellaswag (0-shot)

Hellaswag is designed to evaluate a model’s commonsense reasoning - something that humans do easily, but which is difficult for AI. The model has to predict the next logical step in a scenario, with zero reference examples.

Tool Use

BFCL V2 & Nexus

These benchmarks assess the model's ability to interact with external tools and APIs. This is important when LLMs are integrated into complex workflows or in agentic systems, where they might need to access external APIs, databases or perform specific functions beyond just generating text.

Long-Context Understanding

InfiniteBench/En.MC (128k) & InfiniteBench/En.QA (128k)

These benchmarks test the model’s ability to handle very long contexts (lots of text) - stuff like novels, research papers, or legal contracts. The "128k" refers to the token limit, or how much text the model can keep in memory while performing tasks like multiple-choice questions (MC) or answering questions (QA).

NIH/Multi-needle

Another benchmark for long-context understanding, but specifically designed to test how well the model can handle complex, multi-step queries, especially in medical or scientific contexts.

Multilingual Abilities

MGSM (0-shot, CoT)

MGSM evaluates a model’s performance in math problems across multiple languages, combining both its multilingual and mathematical reasoning capabilities. This is useful for global applications where users need the LLM to solve problems across different languages.

As you can see, these benchmarks target specific abilities that are critical for different kinds of tasks. Depending on your use case, these benchmarks give you a way to compare LLMs based on their strengths and weaknesses. Importantly, it’s not about finding the “best” model overall; it’s about finding the one that performs best for your specific use case.

In the next section, let's dive deeper into each one of these benchmarks and try to understand their relevance and the type of task that they are designed to evaluate a model on.

Benchmarks Deep Dive

General Language Understanding

When evaluating an LLM for “general language understanding”, we’re talking about how well a model can handle a broad spectrum of language related tasks. This could range from academic questions to summarization, paraphrasing, or understanding formal text like what is used in legal documents.

For use cases that need a flexible model - one that can manage various types of language-heavy tasks - it’s performance on these benchmarks is important.

Let’s break down some of the major benchmarks in this category and explore which tasks they’re best suited for.

MMLU (Massive Multitask Language Understanding) – 5-shot

If your project requires the LLM to have broad general knowledge across multiple domains, MMLU is one of the most relevant benchmarks. It tests the model's understanding across a wide variety of academic subjects like history, math, science, philosophy etc. The "5-shot" learning setting means the model gets 5 examples to learn from before it's tested.

When to prioritize MMLU

Knowledge-Intensive Applications: If your use case involves tasks like answering customer questions that span various industries, educational content creation, or training simulations, a high MMLU score is crucial. For example, if you're developing an AI tutor or an LLM for educational platforms, you'll want a model that scores well in MMLU because it indicates strong performance across multiple knowledge domains.

General-Purpose Assistants: In scenarios where the model needs to wear multiple hats (think virtual assistants who answer everything from how to calculate tax returns to explaining the history of the Roman Empire), a solid performance in MMLU can signal that the LLM is versatile enough to handle a wide range of topics.

Use Case: Imagine an organization wanting to deploy an LLM in customer support across different verticals—banking, retail, healthcare, and education. A model that scores highly on MMLU can better manage the diversity of queries without additional domain-specific fine-tuning.

Open-rewrite eval (0-shot, rougeL)

This benchmark evaluates the LLM’s ability to rewrite or paraphrase text, without any prior examples ("0-shot"). The scoring metric, rougeL, measures how well the model's rewritten version aligns with a reference text, assessing both accuracy and fluency in language generation.

When to prioritize Open-rewrite eval

Text Summarization Tools: If your project requires distilling long pieces of content into shorter, more readable summaries—like condensing research papers, news articles, or legal briefs—this benchmark is highly relevant. Models that excel in this area are great for automating content summarization or creating concise executive summaries for business reports.

Paraphrasing for Content Creation: Content creators and marketers often need to rephrase material for different platforms while maintaining the core message. High performance on the open-rewrite evaluation suggests the LLM is effective at paraphrasing content without losing meaning, which is essential for avoiding redundancy while staying SEO-compliant.

Use Case Example: A tool that generates easy-to-read summaries of complex legal documents. A model that performs well on the open-rewrite benchmark will be able to retain key legal concepts while transforming complex jargon into plain language that non-specialists can understand.

TLDR9+ (1-shot, rougeL)

This test is focused on summarization, but with a slight twist. In the 1-shot setting, the model is given one example to learn from before being asked to generate a summary. The same rougeL score applies here to measure alignment with the reference text.

When to prioritize TLDR9+

Automated News Digest: If you are building a tool to provide daily news summaries or social media roundups, a high TLDR9+ score will signal that the model is good at concise summarization, even with limited context. It’s a great indicator that the LLM can take one example of a summary and generalize that skill to similar tasks.

Internal Knowledge Management: For companies with huge internal databases, such as large corporations or research institutions, models that score well on this benchmark can be used to generate short, meaningful summaries of vast knowledge repositories. This is especially useful for creating reports or dashboards that provide a TL;DR of the latest findings or updates.

Use Case Example: An AI-driven newsletter that pulls together the most important highlights from various sectors - tech, finance, healthcare etc. A model that scores high on TLDR9+ is perfect for generating concise, well-structured summaries from lengthy articles, even with seeing just one example as reference.

4. IFEval

IFEval stands for Information Formalization Evaluation, and it tests how well a model handles structured, formal text. This includes legal documents, technical manuals, and any sort of highly specific and specialized writing.

When to prioritize IFEval

Legal and Compliance Automation: If you are working in a highly regulated industries like finance, law, healthcare etc., you will probably need a model that can understand and generate formal language accurately. A high IFEval score shows that the LLM can work with complex, jargon-filled documents and produce coherent outputs, which is critical for tasks like contract generation or regulatory compliance analysis.

Technical Documentation: Companies that produce highly technical products, such as software firms or manufacturing plants, require detailed manuals. LLMs that score well on IFEval can assist in drafting, reviewing, or translating these types of documents into more digestible formats without sacrificing accuracy.

Use Case Example: Using an LLM to automate the generation of employment contracts or NDAs. A high IFEval score demonstrates that the model can produce legally sound, well-structured documents that meet industry standards, minimizing the risk of errors or omissions.

Why General Language Understanding Benchmarks Matter

These benchmarks—MMLU, Open-rewrite, TLDR9+, and IFEval—are like a model's Swiss Army knife for language. They test the ability to handle multiple domains, rewrite or summarize content, and understand formal or technical language. In practice, these benchmarks are crucial when you’re looking for a general-purpose LLM that needs to tackle various tasks across a broad range of scenarios.

If you're considering a model for customer-facing roles, document summarization, or specialized content generation, paying attention to how it scores on these general language benchmarks is your first step in narrowing down the best fit for your use case.

Mathematical Capabilities

When you need an LLM for tasks involving math, you need a model more capable than one which has a general understanding of language.

You’re looking for a model that can follow complex reasoning processes, handle abstract concepts, and deliver accurate solutions. These specialized benchmarks evaluate how well LLMs perform at math-related tasks, particularly in problem-solving contexts that require step-by-step reasoning.

In this section, we’ll explore some key benchmarks used to evaluate the mathematical capabilities of large language models. I’ll also explain which scenarios make these benchmarks important and how they can guide your decision-making when choosing the right LLM for math-heavy applications.

GSM8K (0-shot, CoT)

The GSM8K benchmark evaluates a model's ability to solve grade-school math problems. These aren’t just simple arithmetic questions; they involve logic and reasoning. The 0-shot part means the model is tested without any prior examples, while CoT (Chain of Thought) refers to how well the model can reason through multi-step problems.

When to prioritize GSM8K

Educational Tools: If you're developing AI tutors or virtual assistants aimed at students, a high GSM8K score is a strong indicator of the model's proficiency in basic to intermediate-level math. Models that excel here can handle everything from simple equations to more complicated word problems, making them ideal for educational software that needs to break down and explain concepts clearly.

Customer Support in Financial Sectors: Many customer service queries/bots in the finance industry require accurate numerical reasoning. Stuff like calculating interest rates, loan payments, or investment returns. A model that performs well on GSM8K will be able to assist customers by solving these kinds of problems on the fly, especially in environments where immediate, precise answers are critical.

Use Case Example: A banking chatbot that needs to help customers calculate monthly mortgage payments or evaluate investment options. A model with a strong GSM8K score would be capable of solving such problems without much additional training, providing quick and reliable answers based on the user’s inputs.

MATH (0-shot, CoT)

Similar to GSM8K, but aimed at more advanced mathematics, the MATH benchmark tests the model’s ability to solve higher-level math problems such as algebra, calculus, and geometry. Again, the 0-shot setting means no examples are provided, and Chain of Thought reasoning ability of the model becomes critical for multi-step problem-solving.

When to prioritize MATH

Scientific and Engineering Applications: If you need an LLM to assist with research, engineering, or other technical fields, a strong performance on the MATH benchmark is essential. For example, models with a high MATH score can be used to automate or assist in solving complex equations, conducting simulations, or analyzing scientific data where advanced math is involved.

Data Analytics and Financial Forecasting: If you're working with large data sets or need precise calculations for financial projections, LLMs that perform well in MATH can support analysts by solving advanced mathematical queries, automating reports, and even identifying trends through mathematical models.

Use Case Example: An AI-powered research assistant helping engineers solve problems in structural analysis or fluid dynamics. A model with high MATH scores can break down complex equations, offer potential solutions, or validate calculations in a highly specialized, technical context, saving engineers time and reducing the likelihood of errors.

Why Mathematical Capabilities Matter

GSM8K and MATH benchmarks focus specifically on how well a model can handle math, both in simple, grade-school context and more advanced, technical scenarios. A model’s performance on these benchmarks is crucial when you’re considering applications that require:

Problem-solving and logic: Especially in situations where answers are not just arrived at in a single step, but require a deeper reasoning process.

Automated calculations: From solving simple math problems for customer inquiries, to tackling complex equations in scientific research or finance.

Educational or training environments: Especially if the model needs to explain the steps it’s taking to solve a problem, so that users can understand not just the answer, but the step-by-step process behind it.

When choosing an LLM for math-heavy applications, these benchmarks give you a clear picture of whether a model is up to the task, and how it might handle real-world scenarios that go beyond basic math. If you're in finance, engineering, or education, focusing on models with high scores in these benchmarks ensures you have a reliable tool for challenging math-based tasks.

Reasoning and Problem-Solving

When you think about reasoning and problem-solving, imagine tasks where an LLM must go beyond generic answers and demonstrate deeper cognitive abilities.

When the tasks involve understanding context of a query, making logical inferences, or solving complex puzzles, these benchmarks help evaluate how well a model can navigate tricky, multi-step challenges.

In this section, let’s understand the major reasoning and problem-solving benchmarks and see how they can help you choose the right model for tasks that require more than just simple responses.

ARC Challenge (0-shot)

The ARC Challenge (AI2 Reasoning Challenge) is a set of difficult, multiple-choice questions that test a model's ability to reason through scientific and commonsense knowledge. The "0-shot" setting means that the model has to answer these questions without any prior examples, purely relying on its reasoning skills.

When to prioritize ARC Challenge

Scientific Research and Technical Problem-Solving: If you're working on a project that requires an LLM to assist in scientific research, hypothesis generation, or complex decision-making tasks, choosing a model with a high ARC Challenge score is important. This benchmark assesses how well the model can infer solutions from incomplete or complex information, making it ideal for applications that require logical problem-solving.

AI for Strategic Planning: In industries like business consulting or project management, where strategy and decision-making are key, ARC Challenge performance is very useful. Models that score well in this can support human decision-making by analyzing scenarios, weighing pros and cons, and proposing solutions to complex business challenges.

Use Case Example: A business intelligence tool that uses LLMs to assist executives in making strategic decisions. A model with a high ARC Challenge score would be able to reason through market data, competition analysis, and financial reports, offering logical, evidence-based suggestions for business strategy.

GPQA (0-shot)

GPQA stands for General-Purpose Question Answering. This benchmark measures the LLM's ability to answer a broad range of questions that span multiple domains, often without specific training in any of them. The 0-shot setting here again indicates that the model is being tested on its versatility in answering diverse questions without providing it with any reference examples.

When to prioritize GPQA

Customer Support Systems: For an AI-powered customer service chatbot, a high GPQA score indicates that the model can handle a wide range of customer queries without requiring specialized fine-tuning for every domain. This makes it perfect for handling everything from troubleshooting technical issues, to answering product questions.

General Knowledge Applications: Models that score well in GPQA can serve as general-purpose assistants in environments where they need to cover many topics. Whether you’re building a virtual assistant for knowledge workers, a personal AI assistant, or even a trivia bot, GPQA performance shows how adaptable the model is across various contexts.

Use Case Example: A helpdesk AI for an enterprise software company. A model with a strong GPQA score can answer a range of technical and non-technical questions, helping users solve problems without the need to escalate to human support.

Hellaswag (0-shot)

Hellaswag is designed to test commonsense reasoning. The model is given a partial scenario, and it needs to predict the most likely next step from a set of choices. This benchmark is particularly challenging because commonsense reasoning, something that is simple for humans, can be very tricky for AI models to master.

When to prioritize Hellaswag

Automation of Everyday Tasks: If you're developing AI for tasks like home automation or personal assistant services, commonsense reasoning is critical. High performance in Hellaswag means the model can "think" like a human in everyday scenarios, making intuitive decisions and predictions. Whether it’s figuring out what time to set an alarm based on a calendar event or determining the next logical action in a recipe, this benchmark is a solid indicator of how naturally the model can integrate into daily life.

Creative Content Generation: If your use case involves generating stories, scenarios, or game design, a model’s ability to reason through sequential events becomes crucial. Hellaswag evaluates how well the model predicts logical progressions in narrative or real-world contexts. This is important for creating coherent, engaging content.

Use Case Example: A virtual assistant for smart homes. A model with a high Hellaswag score can predict logical sequences, like when to turn off the lights, based on usage patterns or home activity. This kind of intuitive reasoning makes the assistant feel smarter and more aligned with the user's needs.

Why Reasoning and Problem-Solving Benchmarks Matter

The benchmarks we’ve covered here - ARC Challenge, GPQA, and Hellaswag—are focused on different types of reasoning. They test the LLM's ability to:

Make logical inferences: These are necessary for tasks that involve multi-step reasoning or deduction, especially in environments like research, consulting, and strategic decision-making.

Answer general-purpose questions: This is important for customer service, general knowledge applications, or any setting where the model will encounter diverse, unpredictable queries.

Predict logical sequences: Models that score well on commonsense reasoning benchmarks like Hellaswag are good for everyday automation and creative content generation, where predicting the next step is the most important task.

By looking at these benchmarks, you can assess how well an LLM will perform in tasks that required solid cognitive ability.

Tool Use (Systems Integration)

When deploying an LLM in a real-world environment/use case, it will often need to interact with external tools. This can be in the form of calling APIs, handling databases, or performing specific functions like web searches or calculations etc. Due to this, the ability to communicate with external tools effectively becomes an important benchmark in LLM evaluation.

Models that excel in systems integration tasks can extend their functionality beyond language generation and become powerful, multi-functional assistants that can integrate into various automated workflows.

Let’s break down the key benchmarks used to evaluate tool use and understand when high performance on these benchmarks is essential for various use cases.

BFCL V2

BFCL V2 (Benchmarks for Few-shot Conversational Learning) is a benchmark that evaluates how well an LLM can use external tools to accomplish a task. This could involve calling APIs, retrieving information from databases, or interacting with other software tools to solve problems. A high score in BFCL V2 indicates that the model can quickly learn how to interact with these systems with minimal examples—typically in few-shot settings, where the model is given just a few examples to understand how to use the tool.

When to prioritize BFCL V2

Customer Service Automation: In customer service settings, LLMs might need to pull data from various sources - like retrieving order information, accessing customer records, or updating a database. A model with a strong BFCL V2 score will be able to handle these tool-based interactions easily, reducing the need for human intervention in customer support tasks.

AI-Powered Personal Assistants: Imagine AI assistants like Siri or Google Assistant. These assistants need to interact with calendars, messaging apps, home automation systems etc. A model that scores well on BFCL V2 is ideal for developing such assistants because it shows the model can effectively communicate with and use external applications to perform a variety of tasks.

Use Case Example: A helpdesk chatbot for an e-commerce company. The chatbot might need to access customer accounts, retrieve recent orders, or process refunds. A model with a high BFCL V2 score can execute these tasks by interacting with the company’s back-end systems, providing a smooth customer service experience.

Nexus

Nexus is another tool-use benchmark, but it focuses specifically on multi-step interactions with APIs or systems. In many cases, a single API call or database query is not enough. Nexus evaluates how well a model can perform complex, multi-step actions - for example, retrieving data, transforming it, and then using that transformed data for a subsequent task.

When to prioritize Nexus

Workflow Automation: In industries like IT support, finance, or logistics, the workflows often involve multiple steps that require accessing data, transforming it, and making decisions based on the output. A model with a strong Nexus score can automate these workflows by seamlessly interacting with different tools across multiple stages.

Data Processing Pipelines: If an application involves data collection, processing, and reporting, Nexus is a key benchmark. Models that perform well here can handle the entire pipeline - from pulling data from an API, processing it (like cleaning or categorizing the data), and then generating insights or reports based on that data.

Use Case Example: An AI system designed to automate a company’s financial reporting. The LLM can pull raw financial data from multiple sources, clean it, calculate metrics like monthly revenue, and then generate a final report. A high Nexus score indicates that the model can handle this multi-step process efficiently and accurately.

Why Tool Use Benchmarks Matter

As LLMs move beyond simple text generation and begin integrating with real-world systems, tool use becomes one of the most critical aspects of evaluation. Whether it’s interacting with APIs, querying databases, or performing multi-step workflows, these benchmarks tell you how well a model can:

Connect to external systems: A model that scores well on tool-use benchmarks is capable of fetching information, sending requests, and using external services to accomplish more complex tasks.

Automate multi-step processes: In many real-world scenarios, it’s not just about getting one answer—it’s about managing a sequence of tasks, where each step builds on the last. Models that excel in tool-use benchmarks like Nexus can efficiently automate these sequences.

Handle dynamic, real-time tasks: For customer service bots, personal assistants, or any application where the model needs to make real-time decisions based on external inputs, these benchmarks are crucial for ensuring smooth and accurate interactions.

Long-Context Understanding

Long-context understanding is one of the superpowers that sets advanced LLMs apart from others. While most models can handle short interactions or snippets of text, many real-world tasks require the ability to process and retain large amounts of information over extended periods. Information like entire books, detailed legal contracts, long customer service interactions etc.

Evaluating a model’s ability to manage long contexts is crucial when you need it to keep track of details over multiple paragraphs, documents, or interactions without losing the conversation thread.

Let’s explore the key benchmarks that test an LLM's ability to handle large contexts and understand when a high score on these benchmarks is important for specific applications.

InfiniteBench/En.MC (128k)

The InfiniteBench/En.MC benchmark evaluates a model’s capacity to handle multiple-choice questions based on extremely long documents or data streams. The "128k" refers to the token limit, meaning the model needs to understand up to 128,000 tokens (words or characters) of context.

This is a significant test of the model's memory and ability to retain and reason over large amounts of text without getting overwhelmed or forgetting earlier details.

When to prioritize InfiniteBench/En.MC

Document Analysis: If your use case involves reading and analyzing lengthy documents, such as legal contracts, academic papers, or technical manuals, a high InfiniteBench/En.MC score is essential. Models that perform well here can read large volumes of text and also retain information from early sections to answer questions or make decisions later on.

Compliance and Auditing: In industries like finance or healthcare, compliance documents and regulations are often extensive and complex. An LLM with strong long-context understanding can help by scanning through these documents, identifying key points, and answering regulatory questions without missing crucial details buried in the text.

Use Case Example: An AI system for legal professionals, designed to analyze contracts. A model that scores well in InfiniteBench/En.MC can read entire contracts (which can easily run over 100s of pages), understand clauses, and flag important issues like termination conditions or liability clauses, all while keeping track of key details across the entire document.

InfiniteBench/En.QA (128k)

Similar to the multiple-choice version, the InfiniteBench/En.QA benchmark tests a model’s ability to handle long-context text but focuses on question-answering tasks. The model is expected to answer open-ended questions based on large chunks of information (again, up to 128,000 tokens).

This benchmark evaluates not just memory but also comprehension and synthesis -how well the model can summarize and extract important information from long texts to provide meaningful answers.

When to prioritize InfiniteBench/En.QA

Research and Summarization: For projects that involves synthesizing large amounts of research, whether it's scientific literature, market reports, or legal cases, this benchmark is important. A high score means the model can sift through a lot of information, and provide accurate and concise answers or summaries.

Customer Support Knowledge Bases: In many domains, customer support agents or AI powered chatbots need to reference large databases of information, such as product manuals or troubleshooting guides. A model that scores well on InfiniteBench/En.QA can provide relevant answers even when the source material is extensive, helping users find the information they need without manual searching.

Use Case Example: An AI research assistant that helps scientists or analysts by reading through hundreds of pages of reports and studies, then answering complex questions based on the data.

NIH/Multi-needle

The NIH/Multi-needle benchmark is designed to evaluate long-context understanding in medical and scientific domains, focusing on tasks that require multi-step reasoning over large amounts of technical content.

This benchmark simulates real-world scenarios where the model must handle dense, specialized information and provide coherent responses based on multiple sources or layers of data.

When to prioritize NIH/Multi-needle

Medical Research and Diagnostics: In healthcare, professionals often need to process extensive patient records, research studies, or medical guidelines. An LLM that performs well on NIH/Multi-needle can assist doctors and researchers by navigating through large, complex information, helping them make informed decisions without manually sorting through endless documents.

Scientific Research and Publications: In fields like biology, chemistry, and engineering, long research papers and multi-step methodologies are the norm. A high NIH/Multi-needle score signals that the model can help with interpreting complex procedures, understanding experimental results, and provide insights from lengthy research papers or scientific literature.

Use Case Example: A tool for medical professionals that needs to analyze a patient’s long-term medical history, scan research papers on new treatments, and cross-reference these with diagnostic guidelines. A model with a high NIH/Multi-needle score would excel at managing these large, multi-step tasks and provide actionable insights based on thorough understanding of the information.

Why Long-Context Understanding Benchmarks Matter

These benchmarks - InfiniteBench/En.MC, InfiniteBench/En.QA, and NIH/Multi-needle - focus on the LLM’s ability to handle large volumes of information over extended interactions. They measure how well an LLM can retain and reason with context spread over thousands of tokens, making them particularly important for use cases like:

Document-heavy tasks: Legal analysis, compliance audits, and technical writing often require models to process large, structured documents while retaining critical details from earlier sections.

Long-term customer service interactions: When handling customer interactions that involve multiple queries or references to prior conversations, a model with strong long-context understanding ensures a more cohesive user experience.

Research and knowledge synthesis: For tasks that involve extracting information from long research papers or reports, these benchmarks are important in assessing the model's ability to summarize and reason through the content effectively.

Multilingual Capabilities

Today we need large language models that do more than just handle English - they need to speak many languages fluently, process complex multilingual tasks, and be able to translate between languages easily. For use cases in customer service, content localization, supporting global users etc., the ability of an LLM to understand and generate text in multiple languages is very important.

“Multilingual capabilities” benchmarks test how well a model can manage diverse linguistic challenges, including problem-solving, math, and reasoning across multiple languages.

In this section, let’s explore the benchmarks that evaluate an LLM's multilingual ability and understand the scenarios where they become important.

MGSM (0-shot, CoT)

MGSM stands for Multilingual Grade-School Math, and it evaluates the model’s ability to solve math problems in different languages, using Chain of Thought (CoT) reasoning. The "0-shot" setting means the model is tested without prior examples in a specific language, assessing its ability to tackle mathematical problems with no prior examples or knowledge.

When to prioritize MGSM

Global Educational Tools: If you’re developing educational platforms aimed at students across different countries, MGSM is a good benchmark to consider. A high score in MGSM shows that the model can understand math problems in multiple languages and reason through them effectively, making it suitable for global math tutoring applications.

Multilingual Problem-Solving: Organizations that serve a global audience may need LLMs capable of handling technical or numerical queries in various languages. MGSM performance shows how well the model can solve math-based or logic problems regardless of the language of input prompt, which is critical in customer service or technical support scenarios where users might submit queries in their native languages.

Use Case Example: A multilingual math tutoring app aimed at students from all around the world. A model with a strong MGSM score can guide students through complex math problems in their native language, explaining each step clearly and logically.

Why Multilingual Capabilities Matter

The ability to perform tasks in multiple languages is very important in a range of industries, from global customer support to education and content creation. Models that score well on multilingual benchmarks, like MGSM, demonstrate the ability to:

Understand and generate text in multiple languages, ensuring effective communication with users no matter their location.

Solve complex problems that require logical reasoning or numerical understanding, even when the questions are asked in different languages.

Translate effectively, which is essential for cross-cultural collaboration, content localization, and customer service across language barriers.

Bringing It All Together: Choosing the Right LLM for Your Needs

We’ve explored the core benchmarks that are used to evaluate large language models. To make use of this information effectively, you must focus on the use cases you are interested in, and matching them with the benchmarks that reflect the model’s strengths.

Here’s a high level approach:

Start with Your Use Case

Clearly define the specific task you need the LLM to do. Are you building a customer service chatbot? Do you need a model that can handle complex research or automate financial reports? Maybe you’re developing an AI-powered educational platform or legal assistant. Once you’ve identified the primary use case, you can narrow down the benchmarks that matter most for it.

Evaluate Benchmark Scores

After determining your primary use case, evaluate models based on the relevant benchmark scores. LLM developers like OpenAI, Meta, Alibaba and others publish detailed benchmark results, which can help you compare models side by side. Look for models that not only excel in the areas you prioritize, but also perform consistently well across the board if your application requires versatility.

For example:

If you’re building a customer service chatbot, a model with high GPQA (for versatile question answering) and BFCL V2 (for tool use) scores will be the best option.

For a legal assistant, you will want a model with strong IFEval (for formal text understanding) and InfiniteBench/En.MC (for handling long, complex contracts).

Consider Trade-offs

No model is perfect in every area. Some models might excel at reasoning and problem-solving but fall short in multilingual capabilities. Others may be great at handling long contexts but struggle with real-time tool use. Depending on your needs, you may have to make trade-offs. For example:

If you need deep reasoning but in only a few languages, you might prioritize ARC Challenge performance over MGSM.

If speed and real-time tool integration are more important than handling large documents, you can focus on BFCL V2 rather than InfiniteBench scores.

Test in Your Environment

Benchmarks provide a great starting point, but it’s essential to test the LLM in your real-world environment. What looks good on paper might not translate perfectly to your specific use case. Run pilots or sandbox tests, especially for tasks that involve sensitive data, customer interactions, or decision-making.

If you have reached this point, congratulations! You now have a fairly good understanding of the key benchmarks used to evaluate LLM performance for various use cases.

Comprehensive Guide to Evaluating Large Language Models