The best LLMs for content marketing, websites, and spreadsheets

Lately, the tech world feels a lot like the ‘Battle of the LLMs’ where it seems like a new model is being released frequently. Anthropic recently released Claude Sonnet 3.7, which is dubbed to be the world’s first ‘hybrid reasoning model’. DeepSeek R1 emerged and went head to head with OpenAI’s ChatGPT. The bottom line is, there are many options available. The natural follow-up question is: which LLM should I be using?

While there is an ongoing debate about the impact of AI on job security, it’s fair to say that we need to find a way to work with LLMs in our daily work lives. Again, this leads us to the question: which LLM should you be using?

Deciding on which LLM to use largely depends on your job role, job functions and what tasks you need help with. For instance, if you work with spreadsheets, you might need an LLM that handles structured data effectively, and if you work in content marketing, you might need an LLM that helps synthesize and break down your raw thoughts into engaging copy.

Determining which LLM is the best for your specific task all boils down to the abilities of each language model. Machine Learning engineers have created a set of benchmarks which can help demonstrate how well a specific LLM performs.

In this post, we'll explore the best LLMs for marketers, web designers, and spreadsheet users, as well as which benchmarks actually matter for these fields.

What are LLM benchmarks?

Large Language Model (LLM) benchmarks are standardized tests designed to measure and compare the abilities of different language models. These benchmarks let researchers and practitioners see how well each model handles different tasks, from basic language skills to complex reasoning and coding.

The main reason we use LLM benchmarks is to get a consistent, uniform way to evaluate different models. Since LLMs can be used for a variety of use cases, it’s otherwise hard to compare them fairly. Benchmarks help level the playing field by putting each model through the same set of tests.

LLM benchmarks answer questions like:

• Can this LLM handle coding tasks well?
• Does it give relevant answers in a conversation?
• How well does it solve reasoning problems?

Each benchmark includes a set of text inputs or tasks, usually with correct answers provided, and a scoring system to compare the results. For example, the MMLU (Massive Multitask Language Understanding) benchmark includes multiple-choice questions on mathematics, history, computer science, law, and more. Benchmarks help track LLM improvements over time, identify weaknesses, and guide fine-tuning

Other benchmarks like TruthfulQA test whether models generate accurate and factually correct responses, while GSM8K evaluates numerical reasoning skills. Benchmarks are created by research groups, universities, and tech companies. Many are open-source for accessibility. ARXIV is a great resource to learn more about LLM benchmarks.

Why do we need LLM benchmarks?

LLM benchmarks serve three main purposes:

1. Standardized evaluation - They provide a consistent way to assess LLM models across different capabilities.
2. Progress tracking - Researchers can track whether new models outperform previous versions.
3. Model selection - Users can compare models to determine which one is best suited for specific tasks like content marketing or spreadsheet automation.

How do LLM benchmarks actually work?

LLM benchmarks evaluate models using structured tests designed to assess performance in a controlled manner. There’s usually three steps to establishing benchmarks.

Step 1: Dataset input and testing

Benchmarks include tasks that the model has to complete, such as solving math problems, writing code, answering questions, or translating text. The number of test cases varies by benchmark, ranging from dozens to thousands.

Typically, benchmarks provide a dataset of text inputs, requiring the LLM to process each input and produce a response. Coding benchmarks might include programming tasks like writing specific functions, while some benchmarks use structured prompt templates to guide the LLM’s response.

Most benchmarks come with “ground truth” answers, these are the correct responses in the context of the evaluation. However, alternative evaluation methods exist, such as Chatbot Arena, which was developed by researchers at UC Berkeley SkyLab and LMSYS. Chatbot Arena uses crowdsourced human labels to judge responses.

The LLM does not “see” the correct answers during testing, they are only used to assess the quality of the responses.

Step 2: Performance evaluation and scoring

Once an LLM completes a benchmark, its performance is quantified using scoring metrics. The most common ones are:

• Accuracy metrics such as MMLU are used for benchmarks with a single correct answer. Accuracy is calculated based on the percentage of correct responses.
• Overlap based metrics such as BLEU, ROUGE are used in tasks like translation or free-form text generation, these metrics compare word and phrase overlap between model responses and reference answers.
• Functional code quality, such as HumanEval’s pass@k measures how many generated code samples pass unit tests for programming problems.
• Fine tuned evaluator models like TruthfulQA’s GPT-Judge use an LLM-trained evaluator to classify answers as true or false.

Step 3: Ranking and leaderboards

Once multiple models have been tested, their scores are ranked. Many benchmarks maintain leaderboards, providing an easy way to compare models. Hugging Face’s open LLM leaderboard aggregates benchmark results across multiple sources.

By ranking models across standardized benchmarks, researchers and practitioners can objectively assess how well different LLMs perform and determine which ones are best suited for specific tasks.

Understanding different LLM benchmarks

Benchmarks are standardized tests that measure an LLM's abilities across different tasks. However, not all benchmarks are relevant to content marketing, web design, and spreadsheet automation. Here are the ones that matter:

LLM benchmarks for content marketing & website copywriting

MT-Bench

MT-Bench is a multi-turn benchmark designed to measure the ability of large language models (LLMs) to engage in coherent, informative, and engaging conversations. It assesses how well models can maintain context, follow instructions, and generate meaningful responses across multiple turns in a dialogue. MT-Bench LLMs like GPT-4.

For marketers and copywriters, an LLM needs to sustain logical and compelling narratives, whether for long-form articles, social media threads, or chatbot interactions. MT-Bench helps evaluate:

• Context retention: Does the model maintain coherence across multiple exchanges in a conversation or structured content?
• Instruction following: Does the model adhere to specific content briefs, SEO guidelines, or brand tone effectively?
• Reasoning & relevance: Is the content it generates engaging, logically structured, and free from unnecessary repetition?

GAIA (general AI assistants)

GAIA is designed to evaluate the performance of AI systems beyond simple accuracy. It challenges AI models with complex, multi-layered queries, testing their ability to reason, process multi-modal data, navigate the web, and utilize external tools effectively.GAIA evaluates whether an LLM can pull relevant, up-to-date information from online sources

MMLU (massive multitask language understanding)

MMLU evaluates general language proficiency, which impacts writing clarity and persuasiveness. MMLU helps evaluate:

• General knowledge and contextual understanding: Can the LLM generate factually accurate and contextually appropriate responses across different topics?
• Writing clarity and coherence: Does the model structure its responses logically and maintain readability?
• Persuasive language skills : Can the model generate compelling copy that aligns with marketing goals?

LLMs with high MMLU scores are typically better at crafting well-informed, engaging content that will resonates with your readers

LLM benchmarks for web design & development

When it comes to web design and development, an LLM must do more than just generate text, it needs to understand HTML, CSS, JavaScript, and web design principles. It should also be able to create structured layouts, assist with UX/UI copywriting, and predict the user flow The following benchmarks help evaluate an LLM’s ability to handle web development tasks effectively

HumanEval

HumanEval is a benchmark designed to test an LLM’s ability to generate and interpret functional code. For web development, it evaluates whether a model can write clean, well-structured HTML, CSS, and JavaScript code. This benchmark is particularly useful for:

• Writing semantic HTML and accessible web pages.
• Generating responsive CSS styles.
• Creating dynamic JavaScript functions for interactive elements.

CodeXGLUE

CodeXGLUE is a collection of code-related benchmarks that assess an LLM’s proficiency in how the LLM understands and writes code.

For web development, CodeXGLUE is particularly useful because it tests an LLM’s ability to:

• Generate front-end and back-end scripts in languages like JavaScript, Python, and PHP.
• Understand and optimize existing code, ensuring best practices in performance and readability.
• Automate repetitive coding tasks, such as generating boilerplate code or refactoring inefficient scripts.

LLMs for spreadsheets & data handling

When working with spreadsheets, the LLM needs to handle numerical reasoning, structured data processing, and automation. Additionally, the LLM needs to be able to solve math problems, generate spreadsheet formulas, and interpret complex datasets.

The following benchmarks help assess an LLM’s performance in these areas:

GSM8K

GSM8K is a benchmark that tests an LLM’s numerical reasoning and problem-solving skills. It focuses on grade school math word problems that require logical thinking and step-by-step calculations. This benchmark is useful for:

• Creating financial models in spreadsheets.
• Performing data-driven decision-making using structured calculations.
• Improving Excel and Google Sheets formula generation.

SpreadsheetBench

SpreadsheetBench is a benchmark designed to assess an LLM's proficiency in real-world spreadsheet manipulation tasks. This benchmark evaluates an LLM's ability to:

• Comprehend and execute intricate spreadsheet instructions.
• Navigate and manipulate complex spreadsheet structures.
• Provide answers adaptable to varying data scenarios.

MiMo Table

MiMoTable is a benchmark that focuses on multi-scale spreadsheet tasks. MiMoTable challenges LLMs to:

• Interpret and answer questions based on complex tables.
• Generate coherent textual descriptions from tabular data.
• Perform sophisticated data analysis and manipulation tasks.

Now that we know which benchmarks matter, let’s examine which LLMs perform best for each category.

Best LLMs for content marketing and copywriting

Choosing the right Large Language Model (LLM) for content creation requires the LLM to generate engaging, persuasive, and contextually relevant text. The most effective LLMs for this purpose have been tested on benchmarks like MMLU (Massive Multitask Language Understanding) and MT-Bench.

Here are the three of the top-performing LLMs for content marketing and website copywriting:

Gemini 

Developed by Google DeepMind, Gemini is one of the most powerful AI models for natural language generation. It is specifically designed to handle complex reasoning and generate high-quality long-form content.

Gemini achieved a 90% accuracy score on the MMLU benchmark. The model is particularly strong at:

• Generating engaging, well-structured content that aligns with brand messaging.
• Understanding user intent and tailoring responses accordingly.
• Fact-checking and reducing misinformation in AI-generated text.
• Producing search engine optimized content

Gemini Ultra also excels in multi-turn conversations, meaning it can handle complex writing prompts while maintaining consistency and coherence.

GPT-4

Developed by OpenAI, GPT-4 is one of the most widely used and well-regarded LLMs for content creation. AI writing tools such as Writesonic use GPT-4. GPT-4 is known for its advanced natural language generation, high fluency, and ability to produce human-like responses. GPT-4 has achieved some of the highest scores on the MT-Bench benchmark, making it suitable for indicating its proficiency in maintaining coherent and informative multi-turn conversations.

For content marketers, GPT-4 is particularly useful in:

• Writing high-quality, long-form content that flows naturally.
• Creating marketing copy
• Generating social media content from blog posts

Another key advantage of GPT-4 is its strong contextual memory allows it to follow your previous inputs ensuring that AI-generated marketing copy aligns with your voice and or previous samples.

Claude 3 Opus

Anthropic’s Claude 3 Opus is another leading LLM for content marketing and website copywriting. Claude 3 Opus has been evaluated on MMLU and other benchmarks, where it has demonstrated strong performance in language comprehension and contextual reasoning. Claude 3 Opus is great for:

• Generating informative, engaging, and human-like text that is easy to read.
• Handling complex queries and multi-step reasoning to produce high-quality articles.
• Producing marketing content that aligns with ethical guidelines and avoids misinformation.

Anthropic recently released Claude Sonnet 3.7, which is poised to be the world’s first hybrid reasoning model; early indicators suggest that it could be a game changer for content marketing.

Best LLMs for web design and development

The best models for this domain have demonstrated strong performance in benchmarks such as HumanEval and CodeXGLUE. Here’s an in-depth look at the top-performing LLMs for web development:

Gemini 

Gemini  is a multimodal AI model capable of handling both text and code-based tasks. While primarily designed for general reasoning and content generation, it has also demonstrated exceptional performance in coding-related benchmarks.

Gemini Ultra has been evaluated on HumanEval and performs exceptionally well in generating structured, optimized code for front-end frameworks like HTML, CSS, and JavaScript. Gemini Ultra is also great for:

• Generating responsive UI components, making it useful for web designers working with React, Vue.js, or Tailwind CSS
• Automating layout structuring, ensuring that web pages maintain clean, accessible, and user-friendly designs.
• Handling back-end logic and API integrations, supporting frameworks like Node.js, Flask, and Django.

For web developers looking to build complete, AI-assisted websites, Gemini Ultra offers a balanced mix of front-end and back-end coding support with an emphasis on usability and efficiency.

GPT-4

OpenAI’s GPT-4 is one of the most widely adopted models for web design and development, thanks to its strong language comprehension, advanced reasoning capabilities, and superior code generation skills. GPT-4 consistently ranked among the top models in CodeXGLUE, a benchmark designed to evaluate AI-assisted software development and scripting proficiency.

GPT-4 has scored among the highest on CodeXGLUE, demonstrating its ability to write, debug, and optimize web development scripts.

GPT-4 can also:

• Generate high-quality front-end components, including JavaScript interactions, CSS styling, and HTML layouts.
• Analyze web page structures and suggest improvements for better UX/UI designs.
• Provide structured code explanations, making it easier for debugging

GPT-4 remains one of the most reliable and advanced LLMs available for web design and development.

LLaMA 3

LLaMA 3, developed by Meta AI, is an open-source LLM optimized for language processing and reasoning. While it is not primarily designed for coding, it excels in understanding natural language structures, making it a valuable tool for UX design, sentiment analysis, and AI-driven content generation.

LLaMA 3 can:

• Analyze user feedback and website interactions to help web designers improve user experience (UX).
• Suggest content improvements based on user sentiment.
• Assist with A/B testing content variations, ensuring websites maximize conversions and minimize bounce rates.

LLaMA 3 is not a direct competitor to GPT-4 or Gemini Ultra in coding tasks, its linguistic capabilities make it an excellent tool for enhancing website usability, accessibility, and user engagement.

Best LLMs for spreadsheets

When it comes to spreadsheets, the right LLM can help with automation and  structured data handling. Whether you need assistance with financial modeling, data-driven decision-making, or automating repetitive spreadsheet tasks, the LLM you choose needs to have strong numerical reasoning skills, structured problem-solving, and scripting capabilities.

Benchmarks such as GSM8K, SpreadsheetBench, and MiMoTable help evaluate LLMs in these areas. Here are the top-performing models for spreadsheet and data processing applications:

GPT-4

GPT-4 has demonstrated exceptional performance on GSM8K, making it a great LLM for spreadsheets. GPT-4 can:

• Generate and troubleshoot Excel formulas, Python scripts, and SQL queries for data processing.
• Assist with data-driven decision-making by analyzing large datasets and identifying key insights.
• Automate repetitive Excel tasks such as sorting, filtering, and conditional formatting.

Claude 3 Opus

While GPT-4 excels in mathematical and numerical tasks, Claude 3 Opus is particularly strong in analyzing structured data and automating workflows. Claude 3 Opus is more focused on workflow automation and structured data management rather than raw numerical computations, however it is superior to other LLMs when it comes to spreadsheets.

Claude 3 Opus is great for:

• Categorizing, classifying, and summarizing large datasets.
• Identifying trends and anomalies in financial and operational data.
• Automating report generation and dashboard creation

XGen-7B

Developed by Salesforce, XGen-7B is an LLM designed for handling complex data structures and conducting long-context analyses. While it may not have the same level of mathematical reasoning as GPT-4, it’s optimized for processing and organizing large datasets, making it well-suited for spreadsheet-heavy applications.

XGen-7B can:

• Analyze massive datasets spanning thousands of rows and multiple sheets, making it ideal for big data and business intelligence applications.
• Generate structured reports, summaries, and insights from spreadsheets, reducing the need for manual analysis.
• Integrate with CRM tools, ERP systems, and other enterprise data platforms.

XGen-7B is an open-weight model, meaning it can be fine-tuned for company-specific spreadsheet automation tasks.

Selecting the right LLM depends on the specific task. For content marketing and copywriting, Gemini Ultra, GPT-4, and Claude 3 Opus stand out as the best choices. Gemini Ultra’s strength lies in structured long-form content and fact-checking, making it ideal for SEO-focused writing. Whereas GPT-4 is a top performer for marketing copy, and social media content. while Claude 3 Opus is preferred for human-like, well-structured brand narratives that align with ethical AI principles.

For web design and development, GPT-4, Gemini Ultra, and LLaMA 3 are the leading models. Gemini Ultra also ranks well in HumanEval, offering strong AI-assisted UI/UX development and layout structuring. Although LLaMA 3 is not a direct competitor for coding tasks, is excellent for sentiment analysis, A/B testing, and user experience (UX) optimization.

For spreadsheets and data handling, GPT-4, Claude 3 Opus, and XGen-7B emerge as the top LLMs. Claude 3 Opus is more structured, focusing on workflow automation and compliance-driven data management, while XGen-7B is optimized for handling massive datasets, CRM integrations, and enterprise-scale analytics.

Choosing the right LLM for your needs

The flood of LLMs on the market has transformed the way we create content, develop websites, and manage structured data, but choosing the right model depends on your specific needs:

• For content marketing & copywriting, Gemini Ultra, GPT-4, and Claude 3 Opus are the best choices.
• For web design & development, GPT-4, Gemini Ultra, and LLaMA 3 excel in code generation, UX/UI design, and sentiment-driven enhancements.
• For spreadsheets & data handling, GPT-4, Claude 3 Opus, and XGen-7B lead the charge

However, choosing the right LLM is only the first step, the next step is to integrate your choice of LLM into your existing workflows. Whether you're working with content, code, or data, you need to ensure that you’re automating, optimizing and scaling your productivity.

As more LLMs continue to emerge, weigh up the best LLM for your specific use case based on performance benchmarks. These benchmarks standardize testing across different models, helping you determine which LLM is best suited for their specific needs.