Comparison of LLMs: A Comprehensive Guide

Released in 2018, GPT-1 is widely considered the “first” LLM. Though it was viewed as a massive breakthrough at the time of its launch, it pales in comparison to the LLMs we have available today. For a quick comparison, GPT-1 was trained on 117 million parameters; GPT-4, released in 2023, just five years later? 1.8 trillion.

All of this goes to say, GPT set the trend for LLMs. Since then (as is typical when one innovator does what they do best), other companies have released LLMs of their own. Thus was marked the era of accessible AI, with each year bringing an exponential increase in processing power, generative capabilities, and widespread use.

What Is an LLM?

Before we get into the comparison, it’s important to understand what an LLM actually is. LLM stands for Large Language Model; an LLM is an artificial intelligence machine that is trained on massive amounts of data. Their main function is to interpret user queries and use their training data to modify, synthesize, and generate responses.

What Are the Most Popular LLMs?

Now that we’ve established what an LLM is, let’s talk about which LLMs we’ll be comparing. We’ll be taking a look at the most commonly used LLMs by “regular” users.

In other words, which models are used for standard informational searches, research, product and service recommendations, and other typical search behavior, just in an LLM environment? For brands looking to get ahead of the search game with Answer Engine Optimization (AEO), this is likely where your audience is right now.

By these standards, the most popular LLMs are:

• OpenAI’s ChatGPT (powered by legacy GPT-4o and GPT-5)
• DeepSeek (DeepSeek V3)
• xAI’s Grok (Grok 4)
• Anthropic’s Claude (Sonnet 4 and Opus 4.1)
• Google’s Gemini (Gemini 2.5 Pro, Flash, and Lite)
• Perplexity (Sonar)

Comparison of LLM Models: Differentiators

Marketers may be wondering: how do each of these models stand out in such a saturated space? Clearly, ChatGPT has a leg up on the competition by being the first-to-market, but surely these other models must have some Unique Value Proposition (UVP) that allows them to keep competing with the big dog.

Let’s look at the things that make each of these LLMs ✨special ✨

ChatGPT

• The largest advantage that OpenAI has is longevity as a result of being first-to-market. They’re the most widely adopted LLM because they were the early bird that got the worm, and they continue to lead the charge in terms of new developments to this day.
• OpenAI’s latest GPTs are multimodal by default. They can handle text, imagery, videos, code, and even audio in a single interface (whichever model you happen to be using), making it perfect for diverse workflows and multimodal prompting.
• As the “original” player in the LLM space, OpenAI also beat the competition in the API race, releasing their first one in March 2023. Their models in general are highly customizable, making them a great option for businesses to build tailored AI experiences and integrate them onto their website.
• OpenAI’s models power features natively within major platforms, including Microsoft products, LinkedIn, and others.
• ChatGPT is high-performing; its training data and parameters are updated frequently, which keeps the technology nimble, but can shift visibility quickly

DeepSeek

• DeepSeek is a math and logic specialist. It has been found to consistently outperform other models in complex tasks that include things like scientific reasoning, structured problem-solving, and even coding.
• As a Chinese company, DeepSeek brings a level of globalization and bilingual fluency that is optimized for multilingual AI search.
• The model is incredibly useful for analyzing long documents or regulatory-level content.

Grok

• Natively integrated into X (formerly Twitter), Grok pulls from real-time social media conversations on X, in addition to its training data, as context for its responses. This makes it a solid model to use when the query has to do with trending topics.
• Grok has its own “brand voice” built in, designed to make it sound edgy and unfiltered. Brands be warned; this can be a risky move from a brand narrative perspective.
  
  • In fact, in July of 2025, Grok faced backlash after generating racist and antisemitic content. xAI issued an apology, acknowledging "the horrific behavior that many experienced" and temporarily suspended the chatbot's posting capabilities.
• As the least developed player in the AI model space, Grok is considered to still be “experimental” and can lack transparency. It’s not recommended that Grok be used for most real-world applications; however, if its user base heavily features your target market, it might be something to consider.

Claude

• Claude’s Sonnet 4 and Opus 4.1 excel at reasoning, ranking at the top for GPQA and MMLU benchmarks, especially for nuanced prompts.
• Similar to DeepSeek’s efficiency with large or complex inputs, Claude’s massive context window makes it ideal for analyzing anything from legal documents to entire websites.
• For those working within heavily regulated industries or sensitive use cases like those that exist within healthcare or finance, Claude could be a good choice due to its safety-first design.
• Claude also has a “built-in” brand voice; though its voice is much more well-suited for customer-facing interactions than Grok’s.

Gemini

• The obvious differentiator for Gemini is its tight integration with Google Search, the long-standing top player in the traditional search space, as well as YouTube. Gemini also powers Google’s AI Mode and AI Overviews.
• Gemini excels at multimodal search, with the ability to combine image, code, video, and text formats (whether it be inputs or outputs).
• For enterprise-level users, Gemini’s large context length is ideal for the analysis of large datasets or pieces of media.
• Google’s prowess in the search space also gives it a leg up in terms of evolution; this can be seen with variations in Gemini models (like Flash and Pro), giving users the option to choose the best one for their unique needs.

Perplexity

• The rest of the models in this article are traditional chatbot-based LLMs; Perplexity, on the other hand, functions like a true search engine
  
  • You’ll see later on how this makes it difficult to draw a comparison between Perplexity and the remainder of the models we’re talking about
• Perplexity’s results cite sources automatically for every search, which poses an important advantage for brands in terms of visibility.
• Most LLMs pull information from their training data. Perplexity does have a training dataset, but it prioritizes up-to-date information that is pulled straight from the web using Retrieval-Augmented Generation (RAG). This means newly published content is cited more frequently and with higher accuracy to current events.
• In terms of use cases, Perplexity is widely trusted in academic spaces for research, comparisons, and general information-seeking.
  
  • Note: Perplexity has a documented history of taking citation materials out of context; it’s recommended to add a layer of human QA when using.

How Do We Compare LLMs? The Performance Matrix

We’ve looked at each LLM’s differentiators and main use cases. Now, to “sciencify” this process, we’ll establish which metrics and performance indicators we’ll be using to determine how well each LLM might perform for various scenarios.

As with most research, there must be some contextual considerations taken. For example, a model that can handle extremely complex tasks and queries might have a higher latency, but the trade-off in this instance might be well worth it when compared to a model with lower latency but an inability to complete the task at hand.

Below you’ll find a comprehensive graphic that plots each of the LLMs we talked about above based on their rankings within each of the following performance indicators:

• Task Complexity: Ranked by performance on established coding and reasoning benchmarks like GPQA, MMLU, and HumanEval.
• Model Size: Ranked by publicly available number of parameters or architecture type.
• Latency: The delay between prompt submission and response generation, measured and ranked by time to first token.
• Integration: Ability to integrate the LLM into existing workflows and tech stacks, ranked by extensibility, developer tooling, and ecosystem reach.

Ethical Considerations: Vital to understand for those in heavily regulated industries; ranked by built-in alignment, safety frameworks, and risk transparency.

A Note About Perplexity

You may have noticed that we’ve excluded Perplexity from the graphic above, and you may be wondering why. While the remainder of the LLMs included (ChatGPT, Gemini, Claude, DeepSeek, and Grok) are chatbot-based LLMs by definition, Perplexity functions a little differently; it’s more of a search, synthesis, and citation engine than the rest.

For this reason, we’ve chosen not to include it in the graph, as it caused some data skews that diminished the purpose of the visualization (no hate to you, though, Perplexity).

How to Choose Between LLMs: Use Cases

It’s becoming pretty clear that not every LLM is created equally; that also means that there’s probably a different “best LLM to use” depending on your industry, need, and available materials. Based on the above factors, let’s review a few specific use cases that lean on each model’s strong suits.

ChatGPT: The Generalist

• Integrated Tools & Sales Enablement: SaaS companies can customize GPTs to create AI sales assistants or onboarding bots.
• Compliance Automation: Summarize documents and answer customer questions, maintaining industry standards and compliance throughout the process.
• AI Assistants: D2C brands can easily create customer-facing chatbots that speak like your brand, a great solution for eCommerce sites.
• General Content Needs: ChatGPT is adept at creating a variety of content, from marketing materials to executive summaries and more.

DeepSeek: The Reasoner

• Code Testing: DeepSeek can be used by development teams to generate and test snippets of code internally.
• Forecasting: In finance and other industries where predictive modeling and forecasting are a vital business function, DeepSeek can be leveraged to automate modeling for things that require strong math and coding logic.

Grok: The Social Butterfly

• Social Trend Monitoring: Grok is attuned to whatever is happening on X right now, meaning agile brands can use it to react to moments as they happen.
• Social Listening & Sentiment Summaries: Tracking what customers are saying about both you and your competitors is easy with Grok being so closely connected to real-time social happenings.

Claude: The Safety Officer

• Summarize Documents: Claude can handle large datasets and long content, making it perfect for summarizing complex documents such as those in healthcare or finance industries.
• Regulatory Flagging: Whether it be reviewing contracts or compliance documentation, Claude’s long context window and attunement to safety reduce hallucinations in high-risk use cases.
• Multimodal Generation: Feed Claude large PDFs, pitch decks, and CRM logs, and it can create executive briefs and presentation materials that stakeholders will love.

Gemini: The Integrator

• Process Images & Content: Healthcare professionals can enter radiology imagery and medical notes together in their clinical workflows.
• Integration With Workspace: Because Gemini is already directly integrated into Google BigQuery, Sheets, Drive, Gmail, and more, it becomes an easily accessible productivity and efficiency tool for organizations.
• Creative Creation: Marketing teams use Gemini to generate campaign and website content, for ideation and brainstorming, and even ad content; all thanks to Gemini’s multimodal search and YouTube link awareness feature.

Perplexity: The Researcher

• Real-Time News & Events: Prepping for an investor briefing? Need an overview of economic events? Perplexity is a transparent solution that prioritizes due diligence.
• Verifying Claims: Because of Perplexity’s prioritization of citing its sources, it’s incredibly useful for reviewing research, confirming statistics, and creating works cited.
• Organizing Documentation: For B2B SaaS brands, for example, can leverage Perplexity for customer-facing portals and Help Centers, generally places where factual correctness matters more than creativity. 

What Does This Mean for Brands?

If you’re a brand trying to meet your customers where they are in the era of AI search, be not afraid. It’s pretty likely that your audience (especially if you’re in one of the industries we mentioned above) has already decided what their “favorite” LLM is.

Just like how audiences gravitate towards specific social platforms (think Gen Z on TikTok vs. Millennials on Instagram), the same goes for LLMs. The only difference is, your customers’ choice likely doesn’t depend on their age; rather, it depends on which LLM meets their needs best.

All of this is to say: the main thing for brands to keep in mind when it comes to figuring out which LLM they should focus their Answer Engine Optimization efforts on is what their target market is using already.

Oh, and while you’re figuring that out, make sure you invest in a trustworthy tool for visibility monitoring; and we’ve got one right here for you. It’s called Goodie. You’re gonna love it.

LLM Glossary: Terms & Definitions

In case you need a guide, here are some common LLM-related terms and their definitions to help you understand the complex world of language models:

• Agentic: When an LLM is agentic, it means that it is capable of acting autonomously to take actions within its environment with minimal human interference. An example of this could be an AI agent that automatically adjusts product listings or prices on an eCommerce brand’s website.
• Hallucinations: LLM hallucinations are phenomena where LLMs generate responses that are nonsensical, factually incorrect, or clearly not grounded in reality. An example of this is asking ChatGPT how many “r”s are in the word “strawberry”; there was a time when it would insist that there are only two.
• Multimodality or Multimodal Search: Some LLMs are multimodal, meaning they can understand, process, and generate different types of content. In other words, you can do more than just talk to it as a chatbot. You can feed the LLM different media (imagery, documents, or videos).
• Neural Networks: A neural network is any system modeled after the human brain. Our brains have their own, organic neural networks; LLMs operate on a similar model, but digital. It’s essentially a web of connections that weigh data and signals to generate a human-like output (in the case of LLMs, an answer to a user query).
• Parameter: Remember the comparison between GPT-1 and GPT-4 we made at the beginning of this article? A parameter is a numerical value that a model learns during training. Based on those parameters, an LLM can understand human language patterns and produce relevant responses.
• RAG (Retrieval-Augmented Generation): Retrieval-Augmented Generation is an “add-on” to the base capability of an LLM. It allows the model to retrieve information from external data sources and combine it with its original training dataset. It’s the reason ChatGPT can tell you what happened on the news today.
• Semantic Search: While this concept extends beyond just LLMs, it’s still relevant. Semantic search is a data searching technique that takes into account the meaning and context of a user’s query rather than just the keywords within it. When you search for the “best earplugs for airplanes” on Amazon, there is an inherent understanding that the product you’re looking for likely has a noise-cancellation feature.
• Training Data: An LLM’s training data is the massive amount of text and code that is used to train a Large Language Model.
• Token: A token is the smallest unit of text that can be understood by an LLM. This could be a punctuation mark, a word, or even parts of words. The LLM then processes these tokens through a process called tokenization in order to understand the full meaning of a user query.