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

I won’t probably surprise you if I say that most hyped large language models (LLMs), like GPT and Gemini, aren’t always the best solution for every project. While these models have gained significant attention, simpler, lightweight alternatives—such as rule-based systems, smaller NLP models, or domain-specific algorithms—can deliver results with far less computational effort and cost, and are sometimes just as effective.

So, why do many businesses still try some of the most popular, but not necessarily the best LLM models, for their specific projects before exploring alternatives?

At Flyaps, as an AI-focused software development team, we prioritize finding the best solution for our clients—whether it’s a household LLM or a simpler, tailored approach for specific tasks.

In this article, we’ll help you make an informed choice by:

• Highlighting the most popular LLM models, their features, and when they’re worth using
• Explaining the infrastructure and resources needed for successful LLM adoption
• Comparing LLMs to simpler alternatives to help you determine the best fit for your project

Curious to learn more? Let’s begin by exploring what LLMs are and what they can do for your business.

What are large language models and what are they used for?

Large language models (LLMs) 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 large language models understand the texts they're trained on and generate new content, imitating both the tone and style.

Best large language models’ functions

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

   Large language models 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, large language models 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 companies’ 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.

Read also: Generative AI vs large language models: key differences and when to use.

Large language models have many applications, but with even more options available, choosing the right one can be overwhelming. Let’s explore the most promising and popular language models and their strengths.

Most popular LLMs and their strengths and weaknesses

Even the best LLM models have their unique strengths and limitations. Understanding their capabilities before selecting one for a specific application is a must.

GPT (generative pre-trained transformer) — one of the best LLMs existing

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 transformer 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, released in 2020, is a massive model — ten times larger than its predecessor. It was revolutionary, able to generate text in human and programming languages like Python. In 2022, Microsoft proclaimed its exclusive use of GPT-3.

GPT-3.5, the upgraded version, powers ChatGPT. It has fewer parameters but is fine-tuned using reinforcement learning from human feedback. After generating responses, the model adjusts based on user evaluations. There's also a Turbo version, offering greater flexibility and cost-effectiveness.

GPT-4, the newest addition released in 2023. Its exact parameter count is unclear, with rumors suggesting over 170 trillion. GPT-4 goes beyond language, handling images as well, which is a notable advancement for a large language model.

GPT models can be general or custom. The ones discussed are general. Custom GPT functions like a tailored version of ChatGPT, activating specific abilities to meet particular needs. Users can adjust data and instructions, and the model adapts. Custom models can also be shared with others.

Pros of GPT models:

• Contextual understanding

These models grasp the context of queries, making their responses more accurate and relevant.

• Multimodal capabilities

GPT-4 can generate not only text but also images.

• Versatility

They can adapt to different needs, whether big or small, making them super versatile.

• Code generation

Can generate codes in different programming languages.

Cons of GPT models:

• High computational demand

Training and running GPT models require lots of computing power, which can be tough for some applications.

• Limited nuance

OpenAI's guardrails prevent their models from generating harmful content but, on the other hand, hinder them from being as accurate or nuanced as they could be.

• Bias concerns

Since GPT models learn from human data, they might pick up biases related to race, gender, and more.

LLaMA — one of the top LLMs among open-source language models

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.

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 of LLaMA:

• Scalable options

LLaMA has various sizes for different needs.

• Accessible

Easy to use and to gain access to.

• Resource-efficient

Uses fewer resources than many other models.

Cons of LLaMA:

• Trained on fewer parameters

Since LLaMA was trained on fewer parameters than many other well-known models, it can be less powerful than them.

• Limited Customization

Limited options for customization for developers.

• Non-commercial use

It's only available under a non-commercial license, meaning it can't be used for commercial purposes like marketing or software development.

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 of PaLM:

• Flexible sizing

Smaller sizes available.

• Google integration

Seamless integration into Google's ecosystem.

• Multilingual adaptability

Users can adjust the tone, style, and desired outcomes of generated text for over 100 languages.

• Code generation

Excels at writing and debugging code.

Cons of PaLM:

There is only one key drawback — PaLM models perform slower compared to Bing and GPT-4 in informal language tests.

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 offers two model types: "base" for natural language processing tasks, and "instruct" — for broader tasks. The base Falcon-40B requires significant GPU memory (90 GB) but still less than many LLaMA models. In contrast, Falcon-7B needs just 15 GB and can run on consumer hardware. 

The instruct models (Falcon-7B-Instruct and Falcon-40B-Instruct) are often used as virtual assistants. Developers can also create custom instruct models using community datasets.  

Falcon excels at specific tasks like generating articles and social media posts and creative content such as poems, scripts, and music. For chatbots, Falcon-40B enables natural, conversational interactions, making it perfect for customer support. 

The model is also valuable for data augmentation, creating synthetic data resembling real-world examples. For instance, it can generate synthetic electronic health records for applications like disease diagnosis or treatment outcome prediction.

Pros of Falcon:

• Human-like responses

Falcon sounds more natural and human-like compared to the GPT.

• Commercial use

Can be used for commercial purposes.

• Wide data integration

Uses a specialized pipeline to gather and process a wide range of relevant data from various online sources.

Cons of Falcon:

• Limited language support 

Supports only 11 languages including English, Spanish and German.

• High memory usage

Some of the Falcon models use more memory compared to many other similar sets of models, which can cause stack overflows.

• Fewer parameters compared to the GPT

Cohere

Cohere is an artificial intelligence 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 of Cohere:

• Cloud flexibility

Shows flexibility across multiple cloud platforms, unlike OpenAI, which is exclusively partnered with Microsoft Azure.

• Efficient accuracy

According to Cohere, their large model demonstrates better accuracy compared to larger models, including GPT-3, despite being three times smaller

• Multilingual support

Embeddings can support multiple languages — over 109.

Cons of Cohere

Performance in tasks like offensive language detection or mathematical problem generation is not as strong as ChatGPT's.

Claude 3

Claude 3, Anthropic's latest generative AI model set, offers improvements in large language models 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 of Claude:

• Can identify items on a picture 
• Highly accurate when answering factual questions 
• Accurate in following the given instructions 

Cons of Claude:

• Weak in complex math

Struggles with complex mathematical problems or intricate logic puzzles. 

• Confused by illogical queries

Gets confused when trying to make sense of illogical queries or those that go against fundamental principles.

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 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 of Gemini:

• Superior visuals and code

Gemini outperforms ChatGPT in various benchmarks related to handling visuals, and code.

• Processes video and multimedia data

Can process multimedia data like video, so can be applied across different industries.

• Scalable options

Have three distinct versions tailored to different computational needs.

Cons of Gemini:

Gemini's integration capabilities with existing services still need to be fully realized compared to GPT, which seamlessly integrates with various platforms.

We’ve covered the seven most popular large language models for 2024. Now, let’s explore the key criteria for choosing the right model to meet your needs.

Key factors for choosing the best LLM model

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

Integration with your existing technical ecosystem

When choosing an LLM, consider how it fits into your current tech setup. Some models offer easy-to-use APIs that you can plug right into your applications with minimal effort. Others are open-source and give you more control but require your team to handle deployment and maintenance.

Looking at the image, you can see a range of models from API-based to open-source options. Think about your team's capabilities and resources. Do you have the bandwidth to manage an open-source model, or would an API-based model be more practical for quick integration?

Costs and affordability

Many assume open-source large language models are free, but while there are no token fees, infrastructure costs depend on the model you choose. 

Proprietary (API type) language models, often outperform open-source options for tasks like generating human-like responses. This performance makes them worth the cost for most businesses. Monthly expenses vary by model and usage. For instance, costs differ between GPT-3.5 with a 4K context and GPT-3.5 with a 16K context. Traffic to your product also impacts expenses. On the low end, yearly costs typically range from $1,000 to $50,000, depending on usage and 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

Large language models offer great potential, but data privacy is a growing concern. GPT, Claude, and Gemini might retain data longer than users expect, raising privacy issues. The industry is gradually shifting toward a user-first approach, so it's important to review the security settings of platforms offering 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.