Choosing the Right LLM for the job or use case

Large language models, or LLMs, are tools that use artificial intelligence (AI) to generate natural-sounding text in response to specific prompts. The best-known example of an LLM is ChatGPT, which exploded in popularity in late 2022.

Choosing the most effective large language model (LLM) for a specific use case will be crucial in successful AI implementation for businesses. 

But ChatGPT is far from the only LLM-powered solution out there, and for one reason or another, your business might use a different model depending on your use case or needs. 

Google’s PaLM models, Anthropic’s Claude, and Meta’s LLAMA are just a few examples of potential models that may better fit your needs than OpenAI’s GPT models, the only models used in ChatGPT.

A carefully selected LLM can provide a significant competitive edge, empower employees, improve efficiency and accuracy, and help businesses to make more informed and effective decisions. 

What to consider when choosing an LLM

Here are some simple criteria for choosing between different LLM options.

Use Case

The use case is the most relevant element when deciding on the language model or AI platform your business will leverage. Simply put, what do I need it to do? Consider the following:

  • What am I asking the model to accomplish? Do I need it to parse text or generate insights simply?
  • Does cost limit me? Will I be prompting this a hundred times or millions of times? Do I need a cheaper option?

Performance & Capabilities

First and foremost, you should look at the performance of each large language model. That means testing its text responses for accuracy, readability, relevance, and more. You don’t want to get stuck using an LLM that never gives accurate or relevant answers.

Of course, even the best LLMs will sometimes provide inaccurate information because that’s just a side effect of how LLMs work. But you can still do your best to find one that does so the least often.

Knowledge cutoff

A critical aspect of any LLM is its knowledge cutoff. That is, how recent is the data it’s been trained on? ChatGPT, for example, is still only trained with 2021 data as of 2023, making it a few years outdated. Every LLM will be out of date because training it on new data takes time.

Still, depending on what you plan to use it for, your LLM should be as relevant as possible. That means you should consider the knowledge cutoff of each tool.


Some models allow more customization, for example, fine-tuning the model based on your data. For example, if you wanted the LLM to be capable of writing in your unique brand voice, you could train it using various brand materials so that it would know what your company’s voice sounds like.

If this is important to you, keep an eye out for options or tools that allow you to achieve this goal.

Context Windows

The context window refers to how much you can add to your prompt or message. Some models allow you to send a message with 50 pages of text, while others limit you to 3 to 4 pages. 

You’ll need a more robust context window using generative AI to parse and pull out relevant information from a large document.


Finally (and most obviously), you’ll want to consider pricing. Many LLMs are accessible for free through platforms like ChatGPT, Poe, or TeamAI for a limited basis, but the more you scale your use, the more the costs will come into play. 

Suppose you’re accessing generative AI and language models through a platform like ChatGPT. In that case, you’ll likely be paying a monthly subscription fee, depending on usage, or being cut off from more expensive models. Platforms like TeamAI are geared towards businesses and gear subscriptions towards workspaces vs. the number of users. 

If you’re accessing the model directly through the API, you’ll want to consider the cost of the API usage. While the price may seem low (fractions of a penny) for each message, this can increase significantly when you scale. This makes using a cheaper model (even fractionally cheaper) essential for some businesses.

The best LLMs to choose from

Now that we’ve covered how you can choose the best LLM for you, you might be wondering exactly what your options are. Plenty of choices are available to you, but we’ll introduce some of the best ones below.

OpenAI’s Models


GPT-4 is the most popular large language model (LLM) presently, and not without reason — GPT-4 is trained on a massive dataset of text and code and can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. GPT4  has already been used to create impressive applications, such as chatbots that can hold conversations with humans and generate realistic-looking fake news articles.

GPT-4 has variations that have larger context windows available.

When businesses should consider GPT-4

Businesses looking for a solution with more advanced reasoning abilities should consider GPT-4. It is more expensive than other models, so it may only solve some use cases. 


GPT-4’s direct predecessor is GPT-3.5. If you’ve used ChatGPT or Bing Chat, you’ve used GPT 3.5 (although Bing now uses GPT-4). OpenAI also creates it, and while it doesn’t have as much capability as GPT-4, it’s much faster, returning answers in seconds. That makes it a great alternative to GPT-4, though prioritizing knowledge or speed is up to you.

GPT-3.5 has variations that have larger context windows available.

When businesses should consider 

Use cases that don’t require the same robust advanced reasoning as GPT-4 should consider GPT 3.5 due to the costs.

Google’s Models


A  Google-developed LLM is PaLM with various models designed for specific use cases. These models are accessible through Google’s Vertext AI and include Bison and Gecko. Bison and Gecko refer to the model’s size, with Bison being Google’s “best value in terms of capability and cost” and Gecko being the “smallest and lowest cost model for simple tasks.” 

The capabilities of Palm are similar to that of OpenAI’s models and may even be more effective for some use cases. Bison is immediately priced between OpenAI’s models.

When businesses should consider 

Businesses relying on GPT-4 alone should consider Bison if the capabilities are robust enough, as this option is less expensive.

Anthropic’s Models

Claude Instant

While less popular than OpenAI’s models, Anthropic has powerful and capable LLMs that can achieve similar results. Claude Instant is priced competitively with GPT 3.5 and has similar capabilities (text analysis, text generation, document comprehension, etc.) with the added benefit of a larger default context window.

When businesses should consider 

Businesses should consider Claude Instant for use cases with some price sensitivity but still require a powerful model.

Claude 2

Anthropic’s newest model, Claude 2, is one of the most powerful LLMs with capabilities similar to GPT-4 with an extremely large context window. However, this power does not come without a price, and it is the most expensive option. 

When businesses should consider 

Most businesses will only require Claude 2 for some use cases; however, if you need to work with large amounts of data or text, Claude 2 is the right option.

Other Models

There are a variety of other options out there, most notably Meta’s LLAMA 2, which may also be great for your individual use case.

The best option for your business is to test different models and find a platform that allows you to do this easily. 

TeamAI is model-agnostic – Find the best for your use case.

The LLMs listed above are just a few options in TeamAI. With so many options out there competing with one another, it can take time to choose the best one. That’s why you can benefit from circumventing that problem altogether.

With a generative AI solution like TeamAI, you don’t have to worry about which LLM is best. That’s because TeamAI is model-agnostic, which means that if a new LLM rises as the best next week, TeamAI can switch to that model without any hassle.

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