Mentioning generative AI, ChatGPT probably comes to mind first. Although it’s the most recognized name in the field, ChatGPT is just one of numerous powerful AI models known as large language models (LLMs). These models are designed to understand and generate human-like text. While ChatGPT has dominated much of the discussion, other companies also leverage LLMs. Google has utilized LLMs for AI responses in search results, and Apple has just introduced its LLM-based Apple Intelligence. Beyond these, various genAI chatbots, text generators, and other tools also employ LLM technology.
With so many LLMs available, each offering different capabilities, it can be tricky to pick the best model for your business. To make things easier, we’ve narrowed the list down to the top seven LLMs you should consider.
So, let’s get cracking!
Understanding LLMs: A Quick Overview
Large language models are the driving force behind the rapid advancements in generative AI that took center stage in 2023, although they’ve been around for some time. These sophisticated machine learning systems are built using complex neural networks and trained on vast amounts of data to perform complex tasks like text generation, sentiment analysis, and data analysis.
At their core, LLMs use a statistical model to analyze massive datasets and operate like highly advanced auto-completion tools. When given a prompt, they generate a sequence of logical text in response. The chatbots and applications built on these models don’t simply search for keywords to generate canned replies. Instead, they attempt to comprehend the question and craft a thoughtful answer.
It’s important to note that LLMs are text-based, which is why we’re beginning to see the rise of Large Multimodal Models (LMMs). These models go beyond text and can work with images, handwritten notes, videos, and more. Although LMMs aren’t as widespread just yet, they hold the potential to expand the practical uses of AI significantly.
Types of Large Language Models
LLMs can be grouped into three main categories: proprietary, open, and open-source models.
Proprietary models, such as GPT-4o and Claude 3.5, are some of the most powerful options available. However, they are developed by private companies, which means that their source code, training data, and other technical details are kept confidential. The only way to access these models is through specific applications or APIs, so you can’t run them independently on your own systems.
Open and open-source models, on the other hand, are much more accessible. Notable examples are Llama 3 and Gemma 2, which are available on various platforms like Hugging Face, allowing businesses to download and run them locally. You can even train these models on your own data and develop custom applications. Unlike proprietary models, open-source models allow developers to freely explore their architecture and learn how it functions.
Open vs. Open-Source: What’s the Difference?
The terms “open” and “open source” might seem similar, but there’s a key distinction.
Open-source models come with permissive licenses, meaning you can freely modify and use them, even for commercial projects. However, you’ll need to share any software you build with the same open-source license and give credit to the original creators.
This freedom extends to nearly all uses, whether for a massive tech venture or something more questionable (though you may run into legal trouble with the latter).
Open models come with fewer freedoms. For example, Llama 3 allows for commercial use but limits it to businesses with fewer than 700 million users. It also restricts certain uses, such as developing tools for illegal purposes. Likewise, Gemma 2’s license prohibits users from creating anything that might assist in committing crimes. These restrictions ensure that companies don’t face PR nightmares by having harmful AI tools attributed to their brand.
Versatility of Large Language Models
One of LLMs’ key strengths is their ability to handle a broad range of tasks, mostly related to natural language processing.
Some common uses of LLMs are:
- General-purpose chatbots (like ChatGPT and Google Gemini)
- Summarizing online content, including search results
- Translating text between languages
- Converting written instructions into computer code
- Crafting marketing copy, such as blog posts and social media content
- Analyzing sentiment in customer feedback
- Moderating user-generated content
- Proofreading and improving written content
7 Best LLMs in 2024
Now that we’ve laid the groundwork, let’s explore the top seven large language models for businesses in 2024, carefully curated by Neurond.
1. GPT
OpenAI’s Generative Pre-trained Transformer (GPT) models have been at the forefront of the recent AI revolution. The two latest versions are GPT-4o and GPT-4o mini. Both are multimodal, meaning they can process images and audio in addition to text.
These general-purpose models, accessible through an API, are used by companies across various industries, including Microsoft, Duolingo, Stripe, and Dropbox. While GPT powers many tools, the most well-known application is ChatGPT, a prime demonstration of the model’s versatility.
What makes GPT stand out is its adaptability. Developers can fine-tune the models for specific tasks or use them broadly across applications. For example, GitHub Copilot employs a version of GPT-4 specialized in assisting programmers with writing code, while EinsteinGPT is integrated into Salesforce to enhance customer service by improving employee productivity. In late 2023, OpenAI even introduced the ability for ChatGPT Plus subscribers to create custom versions of GPT, trained on their own data and integrated with real-time databases, further boosting its flexibility.
OpenAI continues to push boundaries with regular updates to the GPT family. While earlier versions like GPT-3.5 were faster and more cost-effective, they sometimes struggled with errors and bias. GPT-4 brought significant improvements in accuracy and intelligence but at the cost of higher latency and usage fees. The latest version, GPT-4o, not only enhances intelligence but also improves response times and reduces costs, making it more efficient than previous iterations.
Out of the box, OpenAI’s GPT models offer a well-rounded solution suitable for various use cases. They are particularly advantageous for users who prefer a reliable tool without the need for extensive training on custom datasets.
2. Gemini
Google’s Gemini is a versatile suite of AI models designed for a range of devices, from smartphones to dedicated servers. This family includes four models: Gemini 1.0 Nano, Gemini 1.5 Flash, Gemini 1.5 Pro, and Gemini 1.0 Ultra. While they can generate text like other language models, the Gemini models excel in handling multiple data types, including images, audio, video, and code. They are optimized for long context windows, permitting them to process larger volumes of text seamlessly.
The Gemini 1.5 Pro and 1.5 Flash models enhance AI capabilities across Google’s applications, including Docs and Gmail. They also power the company’s chatbot, Gemini (formerly known as Bard). Developers can access Gemini 1.5 Pro and 1.5 Flash through Google AI Studio or Vertex AI.
3. Llama
Llama (Large Language Model Meta AI)) is a family of open-source LLMs developed by Meta, the parent company of Facebook and Instagram. Its latest version, Llama 3.1, serves as the backbone for many AI features within Meta’s applications and has become one of the most popular open LLMs available. Users can freely download the source code from GitHub and use it for both research and commercial use.
Although Llama 3.1 is one of the larger open-source models, it remains relatively smaller than many proprietary models, such as GPT-4. This smaller size translates to faster prompt processing and response times, particularly for coding tasks. The 8B model, the smallest version, offers exceptional efficiency while maintaining solid performance.
Llama 8B is capable of fine-tuning with company-specific data, enabling businesses to customize it for their unique needs. It can be easily downloaded and deployed on both desktop and mobile devices without requiring significant computational resources, making it an ideal choice for smaller companies seeking a flexible and cost-effective LLM solution.
4. Claude
Claude by Anthropic is a strong rival to GPT with three models: Claude 3 Haiku, Claude 3.5 Sonnet, and Claude 3 Opus. These models are designed to be helpful, honest, and safe, making them a top choice for enterprise customers. Slack, Notion, and Zoom have partnered with Anthropic to integrate Claude into their services.
What makes Claude stand out is its massive context window. Claude 3 boasts a window of up to 200,000 tokens – roughly 500 pages of text or 150,000 words – compared to GPT-4’s 32K tokens and the 128K limit of GPT-4o and Google Gemini 1.5 Pro. Such a large context window is beneficial for various business applications, including analyzing trends in extensive datasets, summarizing long customer satisfaction surveys, screening job applications, and collaboratively developing ideas or designs.
However, access to Claude comes at a premium. It is available only via API, and the costs are quite high, $75 per million output tokens for Claude 3 Opus, significantly more than GPT-4 Turbo at $30 or the remarkably low $0.90 for Llama 3. While the Haiku and Sonnet versions of Claude 3 are less expensive and offer faster response times, they sacrifice some intelligence in return.
For those who prefer not to use the API, Anthropic offers a free subscription tier that allows limited access to a chat interface at claude.ai, powered by Claude 3.5 Sonnet. Users can unlock more features and higher usage limits by subscribing to the Pro tier. However, a significant drawback of Claude is the exclusive support for the English language, which may limit its global applicability.
5. Mistral
Mistral offers a series of innovative models, including Mixtral 8x7B and 8x22B, which utilize a unique approach to outperform larger models. These models have fewer parameters, enabling them to run more efficiently on less powerful hardware, yet they still achieve impressive results, often surpassing models like LLaMA 2 and GPT-3.5 in benchmark tests. Released under an Apache 2.0 license, all Mistral models are freely accessible for users.
Additionally, Mistral has introduced direct competitors to GPT with Mistral Large 2 and Mistral NeMo, the latter being a collaboration with NVIDIA featuring 12 billion parameters.
Besides, among Mistral’s offerings, Mistral 7B stands out as an excellent choice for the best self-hosted model suitable for both commercial and research purposes. Users can easily download Mistral 7B, deploy it on cloud services, or run it through platforms like Hugging Face.
6. Qwen
Launched in February 2024, Qwen-1.5 is Alibaba’s latest large language model designed to rival Google’s Gemini and Meta’s Llama models in both functionality and affordability. Alongside the standard model, Alibaba has also introduced a chat-focused variant called Qwen-1.5-chat.
Like Llama, Qwen-1.5 is open-source, allowing anyone to download and install it on their own hardware. This makes it an attractive option for developers, especially those on a budget, as the main costs involve initial hardware setup and ongoing maintenance.
Benchmark tests show that Qwen-1.5 consistently outperforms Llama 2 in various scenarios and delivers competitive results against GPT-4. This positions Qwen-1.5 as a cost-effective alternative, offering near-GPT-4 capabilities at a fraction of the price. This model can also be fine-tuned using your company’s own datasets, allowing you to address specific business needs while maintaining complete control over your data.
For customer support applications, Qwen-1.5 can deliver a chatbot that understands customer issues far better than traditional keyword-based systems. By intelligently responding to queries based on your knowledge base, it can enhance first-contact resolution rates and escalate complex issues to human support agents when necessary. Additionally, Qwen-1.5 supports an impressive 35 languages for interaction and offers translation services for over 150 languages, although the token limits vary depending on the language used.
The Qwen-1.5-7B-chat model is currently accessible via a web interface on Hugging Face, while larger models can be downloaded for local use.
7. Cohere
Cohere is an open-weights LLM and AI platform that has become a favorite among large enterprises for building powerful, contextual search engines tailored to their private data. Its advanced semantic analysis capabilities allow companies to input internal data such as sales reports, emails, or call transcripts and retrieve specific insights with ease.
Cohere can be accessed via its API or through Amazon SageMaker, and its models can be deployed on major cloud platforms like AWS, GCP, OCI, Azure, and Nvidia, as well as in on-premise environments.
Cohere excels in creating knowledge retrieval applications within enterprise settings, such as company-wide search engines that assist professionals in answering business-related questions across sales, marketing, IT, and product development. The platform is user-friendly, with extensive support documentation to facilitate developer integration into business applications.
Also known for its high accuracy, Cohere is particularly well-suited for creating knowledge bases that inform business strategies and support critical decision-making processes.
As for pricing, Cohere offers a free version, along with a Production tier that charges per 1 million tokens of input and output. For highly customizable needs, businesses can contact Cohere’s sales team for a quote on their Enterprise tier.
How to Choose the Best Large Language Model for Your Business?
Selecting the right large language model for your business involves careful consideration of several factors. The ideal LLM should align with your specific needs, budget, and available resources. Here are key steps to guide the decision-making process:
- Identify your use cases: Determine the specific applications that are most important for your business. Whether it’s content creation, customer support, data analysis, or another function, understanding your priorities will help narrow down the options.
- Evaluate features and capabilities: Assess the features offered by different LLMs. Look for foundation models that provide high accuracy, customization options, and integration capabilities with existing systems. Some models may excel in certain tasks, such as text summarization or code generation, so ensure the model aligns with your requirements.
- Consider budget and affordability: Assess the cost of deploying different LLMs. Some models may offer robust features at a higher price, while others might provide essential functionalities at a lower cost. Determine what fits your budget while still meeting your needs.
- Research and stay informed: The LLM landscape evolves rapidly, with new models and updates frequently released. Conduct extensive research on the latest developments and user reviews to make an informed decision.
What Is the Future of LLMs?
The future of large language models is poised for significant growth and innovation. Major tech companies, including Apple, Amazon, IBM, Intel, and NVIDIA, are actively developing or testing LLMs that are expected to enhance enterprise operations. While these future models may not garner the same public attention as more well-known versions, they will likely be widely adopted for internal use and customer support applications.
Furthermore, the development of efficient LLMs optimized for lightweight devices, such as smartphones, is on the horizon. For instance, Google’s Gemini Nano is built to run certain features on the Google Pixel Pro 8, while Apple’s forthcoming Intelligence aims to leverage similar capabilities on Apple devices.
Another exciting trend is the rise of large multimodal models integrating text generation with other modalities like images and audio. Notable examples include GPT-4o and Google’s Gemini models, both of which are among the first widely deployed LMMs, with further capabilities yet to be fully realized.
While predicting the exact trajectory of LLMs can be challenging, the current advancements suggest a future where powerful GenAI tools become increasingly accessible. This has the potential for even more groundbreaking developments, including the possibility of artificial general intelligence within years to come.
Final Thoughts
The rapid evolution of large language models is evident, marked by an increasing number of parameters and distinct traits that cater to many applications. While GPT-4 may dominate public discourse, numerous alternatives are available, each with unique features, strengths, and limitations. It’s crucial to select the LLM that best automates your most time-consuming language-related tasks, seamlessly integrates with your existing technology stack, and aligns with your business goals – whether that’s enhancing marketing output or accelerating data analysis.
At Neurond, we specialize in leveraging the best large language models, which are showcased through our internal virtual assistant, Neurond Assistant. Choosing the right technology partner to implement an LLM into your digital infrastructure is a significant step toward success. Let Neurond drive impactful results for your project today!
