7 Best LLM Models for Your Business: How to Pick the Right One for Specific Needs

11 min read
7 Best LLM Models for Your Business
Best LLM Models for Your Business
Contents

Large language models are not for everyone. Despite the buzz GPT and Gemini provoked, many alternative options require smaller datasets and can perform simpler tasks like sentiment analysis or underlying themes or topics (so-called topic modeling). So, why do many businesses still try LLM for their projects before looking for those alternatives?

Despite being an AI-focused software development team, we at Flyaps are always looking for the most reasonable option for our clients, be it an LLM or a simpler solution. That's why in this article, we've decided to break down the most popular large language models that our clients chose for their projects and explain not only what features they can provide you with, but also what infrastructure they require for successful adoption. Interested? Then let's start with a quick breakdown of what an LLM can be used for and what it is in general.

What is an LLM in AI and what is it used for?

Large language models are the type of generative AI models that focus specifically on analyzing and generating text-based data. These models require a large dataset to be properly trained and use deep learning techniques, including specialized neural networks called transformers. These transformers help LLMs understand the texts they're trained on and generate new content, imitating both the tone and style.

Though LLM is a narrower concept than generative AI, its range of applications is pretty extensive, from DNA research and sentiment analysis to online search and chatbots. Let’s look at the functions these models usually being used to perform.

  1. Translation. Models like GPT-4 completely overshadowed tools like Google Translate, especially for European languages, as they are more accurate in understanding idioms and context.
  2. Content creation. LLMs dramatically changed marketing by automating the generation of various content types, from blogs to social media posts.
  3. Alternative search tool. Just like Google gives instant responses to users' queries when showing its knowledge panel, LLMs also generate answers based on their understanding of the question and context. This way, LLMs can provide tailored responses that may not be available through static search engine results.
  4. Virtual assistants and customer support. LLMs that are trained on the data about specific company's processes and products can be used for improving customer support. Such models are able to provide instant answers to the most common questions and, freeing up the customer support team for more complicated tasks.
  5. Code generation. Models like Mixtral 8x7B Instruct can generate code in various programming languages, significantly cutting development time.

For more information, read our dedicated article on generative AI vs large language models: key differences and when to use.

As you can see, there are a lot of use cases of LLM, but there even more models on the market, so it can be daunting to choose one that perfectly meets your needs. Therefore, let’s look at the most popular and promising models in 2024 and what they are good for.

GPT (generative pre-trained transformer)

GPT (generative pre-trained transformer)
GPT (generative pre-trained transformer)

GPT, is a series of models created by OpenAI. They have been trained on huge amounts of Internet data and are based on a transformer architecture mentioned prior. These models are not small. GPT-3, for example, has 175 billion parameters (settings that define specific functions of the models).

Even though GPT-4 and its Turbo version are the latest ones, it would still be worth talking about GPT-3 and GPT-3.5. But, one thing at a time.

GPT-3 was released back in 2020. It's a massive model – ten times larger than its predecessor. It was revolutionary for that time as it could understand and generate not only texts on traditional human languages but also code on programming languages like Python. In 2022, Microsoft even proclaimed its exclusive use of GPT-3.

GPT-3.5 is the upgraded version of GPT-3 that powers ChatGPT. It has fewer parameters but is fine-tuned using reinforcement learning from human feedback. It means that after GPT-3.5 generates responses, those responses are evaluated by users, and the model adjusts itself based on that feedback. There's also a Turbo version, an even more modernized GPT subset in terms of flexibility and cost-effectiveness.

Last but not least, we've got GPT-4, the newest addition released in 2023. Unlike its predecessors, GPT-4's parameter count is a bit of a mystery, with rumors swirling that it's got over 170 trillion. Moreover, it’s not just about language anymore, as GPT-4 can handle images too, which is impressive for LLM.

The GPT models can be general and custom. Those we’ve just discussed are general. When it comes to custom GPT, think of it as a version of ChatGPT that just activates specific parts of ChatGPT's abilities to suit particular needs. Users can adjust the data and instructions, and the model will adapt accordingly. They can even share their custom models with others.

Pros and cons of GPT models
Pros and cons of GPT models

LLaMA

Large language model Meta AI (LLaMA)
Large language model Meta AI (LLaMA)

Large language model Meta AI (LLaMA) is an open-source solution that everyone can find on GitHub. It comes in various sizes, including smaller ones requiring less computing power than GPT. The biggest version comes with 65 billion parameters.

The feature Llama 2 model that Meta is proud of is its ability to create text that's safe and free from harmful content, all without needing extra instructions from users.

Naturally, the most obvious tasks for LLaMA would be writing articles, social media posts, novels, or video scripts. It is especially good for summarizing without missing important information. Although the model theoretically supports over 120 languages, the quality will be higher for some languages (English, German, French) than for less widely spoken languages such as Polish or Greek.

Pros and cons of LLaMA
Pros and cons of LLaMA

PaLM 2

Pathways language model 2 (PaLM 2)
Pathways Language Model 2 (PaLM 2)

Developed by Google, Pathways Language Model 2 powers various functions across Google's platforms, including Docs and Gmail, handling most search queries. The model has a massive 540 billion parameters but also offers smaller versions with 8-62 billion parameters.

The model has access to the Internet through Google, which allows the model to generate responses based on updated data. To compare, GPT-4 has more limited Internet access since it uses Bing AI.

PaLM 2 has some useful features that make it so popular that it appears on every list of top LLMs. For example, the “Filter” option helps users to narrow down search results by specific criteria like date, type, or relevance. The "Do more" feature gives access to additional tools and capabilities like highlighting key points, summarizing documents, or suggesting further reading for a deeper understanding. "Set reminders" feature provides notifications about updates or new information related to chosen topics to stay on top of the latest developments and relevant news.

The application of PaLM 2 is pretty diverse, so we will only mention the most popular ways of using it. Firstly, businesses that operate globally use this model for accurate translation of documents, emails, and even literary works. Software developers can generate code snippets, functions, or even complete modules in various programming languages with PaLM 2. What’s more, the model not only writes code, but also suggests improvements, identifies bugs, and translates code between languages. Medical researchers and physicians can use Med-PaLM 2 to identify patterns in medical literature and diagnose diseases.

Pros and cons of PaLM
Pros and cons of PaLM

Falcon

Falcon
Falcon

Falcon is one of the most powerful open-source models that definitely outranks LLaMA. It has a maximum of 40 billion parameters but smaller versions with one to seven billion parameters are also available. Since it's offered under the Apache 2.0 license, Falcon can be legally used for commercial purposes.

Falcon has two types of models, which are "base" for language tasks, and "instruct" - for other task types. The base model Falcon-40B needs a lot of GPU memory (90 GB), but it is still less than many of LLaMA’s models. On the other hand, Falcon-7B (another base model) only needs around 15 GB and can be used even on regular consumer hardware.

The instruct versions of these models (Falcon-7B-Instruct and Falcon-40B-Instruct) are usually used as virtual assistants. Developers can also build their own custom instruct version based on these two as well as datasets built by the community.

Falcon works well not just for traditional LLM tasks like generating articles and social media posts but also for creating imaginative content such as poems, scripts, and music. When used for developing chatbots, Falcon 40B makes the system capable of holding conversations in a simple and human-like style. This is especially important for customer support.

The model is also valuable in data augmentation tasks. It can create synthetic data that closely resembles real-world data. For example, by generating synthetic electronic health records with real patient information, Falcon can be used for disease diagnosis or treatment outcome prediction.

Pros and cons of Falcon
Pros and cons of Falcon

Cohere

Cohere
Cohere

Cohere is an AI startup that, apart from being an AI SaaS platform, offers several LLMs including Command, Rerank and Embeddings. The main goal of Cohere’s models is to be flexible and adaptable to both simple tasks (like text classification) and complex tasks (like question answering). The key to their effectiveness lies in attention mechanisms that allow models to focus on important parts of the text when needed. By being programmed to adapt to the context, Cohere models are designed to understand the subtle nuances of language, including tone, style and implied meaning.

Cohere’s models are perfect for enhancements in search and retrieval systems for various applications. The models can be added to compute relevance scores for documents retrieved in a search. The retrieved documents are based on their semantic similarity to a search query.


Tasks like automating customer service and making content also can be automated with Cohere.

Pros and cons of Cohere
Pros and cons of Cohere

Claude 3

Claude 3
Claude 3

Claude 3, Anthropic's latest generative AI model set, offers improvements in LLMs like comprising Opus, Sonnet, and Haiku. It can handle visual data such as photos, charts, and diagrams and works for real-time interactions like live chat support and quick text completions. Moreover, the newest version comes with better performance across all models, with Haiku offering the quickest and most cost-effective performance.

Pros and cons of Claude 3
Pros and cons of Claude 3

Gemini

Gemini, the former Bard, is a group of LLMs created by Google AI. There are three models in the Gemini series: Gemini Nano, Gemini Pro, and Gemini Ultra and they are made to work not only on servers but on devices like smartphones as well. Besides generating text like other LLMs, Gemini models can also understand and analyze images, audio, and video, without needing extra tools or modifications.

Gemini performance compared to GPT
Gemini performance compared to GPT

Gemini models are great for website development. They can review the site content and how people interact with a website to plan out an easy-to-use layout. This enhances the digital experience for potential clients, increasing the chances of them signing up for services or buying products.

Gemini was also put to the test in the healthcare field but showed poor results in complex diagnostic tasks and interpreting medical images.

Pros and cons of Gemini
Pros and cons of Gemini

So, we have reviewed seven of the most powerful large language models for 2024. Now that you have some idea of what they can do, let's talk more about the criteria you should look at to choose the right model for your specific needs.

Things to consider when choosing an LLM

When picking the model for your project, it’s better to consider the five following factors.

Integration with your existing technical ecosystem

Each model offers different APIs, compatibility levels with your tech stack, and varying resource requirements for deployment and maintenance. It means you need to analyze your team's current workflow and systems to determine the specific integration needs.

Team collaboration type (API or open-source)
Team collaboration type (API or open-source)

Costs and affordability

It’s a common misconception that open-source LLMs are free. Surely, you don't have to pay for using tokens. However, you'll need to cover the infrastructure costs that depend on the model you pick.

Proprietary (API type) LLMs work better than open-source models for chat completion tasks like generating human-like responses. As a result, the majority of businesses are willing to pay for them rather than just use open-source options. The monthly costs vary depending on the model and how much you use it. For example, for GPT-3.5 with a 4K context and GPT-3.4 with a 16K context, the costs will differ. Additionally, the amount of traffic your product receives also affects the costs. Overall, on the low end of usage, the yearly expenses range from $1,000 to $50,000, depending on the model.

Scalability

Understanding how well a large language model can adapt and handle larger workloads as demand grows is crucial. That's where large language model operations (LLMOps) come in. LLMOps is basically a set of tools and methods used to make sure language models run smoothly and efficiently regardless of challenges like increased demand. For instance, Meta used LLMOps to create Code Llama, and Google used LLMOps to improve PaLM 2.

Scalability can also be achieved by using pre-built models tailored to your specific industry. At Flyaps, for example, we have a variety of LLM-driven tools for recruitment, logistics, or fintech.

Data privacy and security

Even though LLMs have a lot of potential, there's a growing concern about how they handle data privacy. GPT, Claude and Gemini might keep your data longer than you'd like, which doesn't match what users expect for privacy. However, the industry is slowly but steadily changing towards a users-first approach, so you need to check the security settings of models and platforms that offer these models.

Final thoughts

LLMs are rapidly evolving beyond their basic function of focusing solely on textual data. They can now process images and even audio. With the number of features they use and the number of ways they can be applied growing steadily, it can be difficult to choose the best large language model.

Feeling overwhelmed with the amount of technical details that need to be taken into account when choosing the right LLM? We’ve got you covered! Just drop us a line and we will take care of that.