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Large language models (LLMs) are advanced software architectures that use AI technologies such as deep learning and neural networks to perform complex tasks like text generation, sentiment analysis, and data analysis.
Able to understand and generate human-like text, the best LLMs aid in tasks like writing social media posts and ad copy, crafting personalized responses to customer inquiries, summarizing data for decision-making, and even helping your team come up with new ideas that drive innovation. The top LLMs can be integrated into your current software platforms to improve their efficiency and effectiveness and open up new functionality and automations.
Here are my picks for the best large language models for your business:
- GPT-4: Best for Creating Marketing Content
- Falcon: Best for a Human-Like, Conversational Chatbot
- Llama 3.1: Best for a Free, Resource-Light, Customizable LLM
- Cohere: Best Enterprise LLM for a Company-Wide Search Engine
- Gemini: Best for an AI Assistant in Google Workspace
- Claude 3.5: Best for a Large Context Window
Best Large Language Model Software: Comparison Chart
When evaluating large language models for your business, it’s important to learn about each tool’s developer, parameters, accessibility, and starting price.
A note on parameters: Though greater parameter size does typically signal higher accuracy of an LLM, remember that you can fine-tune most of these AI tools on your own company-, task-, and industry-specific data. Also, some AI companies offer several LLM models, differing in size, with the lower-parameter versions on the lower end of the pricing scale.
| Developer | Parameters (of Largest Model) | Accessibility | Pricing | |
|---|---|---|---|---|
| GPT-4 | Open AI | 1.7 trillion | Chat GPT (uses 3.5, must upgrade for 4) and the OpenAI API | $20 per month for access to GPT-4 |
| Falcon | Technology Innovation Institute (TII) | 180 billion | Open Source (available on Hugging Face and Amazon SageMaker) | Free |
| Llama 3.1 | Meta | 405 billion | Open Source (download to desktop) | Free |
| Cohere | Cohere | 52 billion | Open Source (Cohere API is the easiest access option) | Free |
| Gemini | 1.56 trillion | Google Gemini App or Gemini API | Free | |
| Claude 3.5 | Anthropic | Unrevealed | Claude AI app and Claude API | Free |
TABLE OF CONTENTS
GPT-4
Best for Creating Marketing Content
OpenAI’s GPT-4, accessed typically through the AI tool ChatGPT, is an advanced natural language processing model that’s also one of the most popular LLM models on the market. Compared to other LLMs, its combination of large-scale pretraining, contextual understanding, fine-tuning capabilities, and advanced architecture makes GPT particularly adept at writing detailed, sophisticated responses to your prompts, making it a great assistant to any marketer.
By training GPT on your brand’s tone and style, you can have it generate text that fits your specific style and be easily assimilated into email campaigns, ad copy, social media posts, presentations, and other external and internal content for your business. And with its new image reader, you can even upload an ad image and ask it to write a clever caption. Even as the competition grows fiercer, GPT-4 remains one of the best LLMs on the market.
Why I Picked It
GPT-4 is an advanced API-based LLM that you can access for as low as $20 per month. And it’s remarkably easy to use via the mobile and web chatbot application, Chat-GPT. When it comes to producing marketing content that seems human-written, it’s second to none. The responses are often brimming with ingenuity and specific examples, provided you prompt it effectively.
GPT-4’s accuracy, wide-ranging knowledge base, and fast delivery of information make it a great research assistant. Whether you’re trying to learn more about the pain points of your target audience or nature symbolism in classic poetry, it quickly provides you with precise answers in a digestible format that resembles a blog post.
Pros and Cons
| Pros | Cons |
|---|---|
| Free basic version with Chat-GPT | Occasional hallucinations |
| Can understand and create visual information | Needs skilled prompts to produce desired outputs |
| Coherent, detailed text outputs | Requires subscription for advanced features |
Pricing
- ChatGPT-3.5: Free version
- ChatGPT-4 Plus: $20 per month (create custom chatbots, access latest upgrades, image generation, and generally more intelligent responses)
Features
- Generate articulate, creative text
- Edit and optimize copy
- Summarize text and pictures
- Conduct market analysis
- Data analytics (via Python code generation)
- Do keyword research
- Data science applications (perform K-means, eliminate outliers, etc.)
- Can handle over 25,000 words of text
- Write code
- 1.75 trillion parameters
To learn more about this leading LLM, read the full review of ChatGPT 4.
Falcon
Best for a Conversational, Human-Like Chatbot
Accessed mainly through Hugging Face, Technology Innovation Institute’s Falcon is the best open-source LLM model to use as a human-like chatbot, as it’s designed for conversational interactions with natural back-and-forth exchanges.
Trained on dialogues and social media discussions, Falcon comprehends conversational flow and context, allowing it to deliver highly relevant responses that take into account what you’ve said in the past. In essence, the longer you interact with Falcon, the better it “knows you” and the more use you can gain from it.
This artificial intelligence learning capability makes Falcon ideal for AI chatbots and virtual AI assistants that provide a more engaging, human-like experience than ChatGPT.
Why I Picked It
Falcon is one of the highest-performing open-source LLMs on the market, consistently scoring well in performance tests. It’s also one of the most highly customizable, making it ideal for organizations that want to customize the LLM and use it to deploy applications that integrate into their current operations and align with their overall strategy. Further, Falcon is relatively resource-efficient thanks to a partnership with Microsoft and Nvidia, which has helped it optimize its hardware usage.
Pros and Cons
| Pros | Cons |
|---|---|
| Open to commercial and research use | Fewer parameters than GPT |
| Highly conversational user experience | Supports only a handful of languages |
| Realistic human language generation | Falcon 180-B is resource intensive to run |
Pricing
- Falcon is a free AI tool and can be integrated into applications and end-user products
Features
- Create human-like textual responses
- Track context of the ongoing conversation
- Fine-tunable base model
- Answer complex questions
- Translate text
- Summarize information
- Integrate it at no cost into your business applications
- Language translation
For more information about generative AI providers and their LLMs, read our in-depth guide: Generative AI Companies: Top 8 Leaders.
Llama 3.1
Best for a Free, Resource-Light, Customizable LLM
Meta AI’s Llama 3.1 is an open-source large language model that can assist with a variety of business tasks, from generating content to training AI chatbots. Compared to its predecessor Llama 2, Llama 3.1 was trained on seven times as many tokens, making it less prone to hallucinations.
Despite being one of the larger open-source models, Llama 3.1 is still relatively small compared to many closed-source models like GPT-4. As a result, it tends to run faster in terms of prompt processing and response time, especially for coding tasks. This is especially true for the 8B model, its smallest model, which offers incredible efficiency without sacrificing too much in performance.
Designed to be fine-tuned using your company- and industry-specific data, Llama 8B can be downloaded for free to desktop or mobile devices and customized to users’ needs without using many computational resources. This makes it a great option for smaller businesses that want a free and adaptable LLM that’s easy to deploy.
Why I Picked It
Llama 3.1 is a highly adaptable open-source LLM that comes in three sizes, enabling you to pick the one that best aligns with your computational requirements and deploy it on premise or in the cloud. It’s also highly adept at analysis and coding tasks, often scoring highly in areas related to mathematical reasoning, logic, and programming.
LLama 3.1 also offers synthetic data generation, a service that allows you to use 405B data to improve specialized models for unique use-cases. Overall, the tool is a strong competitor in the open-source enterprise LLM market.
Pros and Cons
| Pros | Cons |
|---|---|
| Fast and resource-efficient | Output may not be as creative as GPT’s |
| Free and open-source | Smaller parameter size than comparable tools |
| High scores in reasoning and coding tests | May perpetuate existing biases in responses |
Pricing
- Open-source LLM and free for research and commercial use
Features
- Advanced reading comprehension
- Text generation
- Company-wide search engines
- Text auto-completion
- Data analysis
- Efficient coding assistant
- 128k context window
- Multi-lingual support
Cohere
Best Enterprise Solution for Building a Company-Wide Search Engine
Cohere is an open-weights LLM (which means its parameters are publicly accessible) and enterprise AI platform that is popular among large companies and multinational organizations that want to create a contextual search engine for their private data.
Cohere’s advanced semantic analysis allows companies to securely feed it company information—sales data, call transcripts, emails, etc.—and then, with a quick search, find answers to questions like “What were Q4 margins in the Western US?”
This streamlines intelligence gathering and data analysis activities, allowing your team to make total use of the enterprise data you capture. You can access Cohere through their API or via Amazon SageMaker. Cohere’s models are available for companies to deploy publicly on AWS, GCP, OCI, Azure, and Nvidia, as well as via VPC or a company’s on-premise environment.
Why I Picked It
Cohere’s impressive semantic analysis capabilities make it a top LLM for creating knowledge retrieval applications in enterprise environments, such as company-wide search engines that help professionals get answers to business questions around sales, marketing, IT, or product. It’s also designed to be easy to use, offering extensive support documentation to help developers integrate the technology into their business applications.
Cohere is also known for its high level of accuracy, which is essential if it’s used to create a knowledge base that gives answers that will be used to guide business strategy and make high-stakes decisions.
Pros and Cons
| Pros | Cons |
|---|---|
| High-quality semantic analysis | More expensive than most LLMs |
| Data and searches are kept private | Free version is mostly for testing |
| Highly customizable | Ill-suited for smaller businesses |
Pricing
- There is a free version, and then the Production tier, which offers three products (command, rerank, and embed) and charges per 1M tokens of data output and input
- Must call Sales for a quote on their highly customizable Enterprise tier
Features
- Designed for enterprise applications
- Natural language understanding
- Semantic analysis and contextual search
- Content generation, summarization, and classification
- Supports over 100 languages
- Advanced data retrieval (re-ranking)
- Deployment on any cloud or on-premise
Gemini
Best for an AI Assistant in Google Workspace
Gemini is a large language model, content generator, and AI chatbot within Google’s Gemini AI suite. It’s multimodal, so it can understand not only text but also video, code, and image data.
While its basic version is free, its big differentiator is “Gemini for Google Workspace,” an AI assistant that’s connected with Google Docs, Sheets, Gmail, and Slides, thus opening up a whole set of use cases for Google Suite users, such as building slideshows in record time.
Starting at $20 per month, you can use Gemini Advanced to easily find and draft documents, analyze spreadsheet data, write personalized emails, conduct market research, and more.
Why I Picked It
Gemini AI’s seamless integration with the Google Suite makes it an incredibly useful personal assistant for business professionals who regularly use Google Docs, Slides, Sheets, and Gmail. With it, users can increase the production speed of anything from a branding deck, product description, or follow-up email. Backed by Google’s resources, the LLM is exceptional at natural language processing tasks and this strength is likely to continue improving in future iterations.
Pros and Cons
| Pros | Cons |
|---|---|
| Highly affordable option | Gemini Pro (free version) can lack accuracy |
| Connects seamlessly with Google apps | Requires significant computational resources |
| Impressive reasoning capabilities | Slightly glitchy long video interactions |
Pricing
- Offers free version of Gemini AI with basic functionality
- Gemini Advanced, the Premium tier, costs $19.99 per month (gain access to Gemini 1.0 Ultra, Gemini Live, advanced Google Suite features, and functionality to do complex tasks)
Features
- Conversational AI chatbot
- Creates presentations easily
- Generates content
- Analyzes reams of data
- Multimodality
- Google Workspace AI assistant
Claude 3.5
Best for a Large Context Window
Available through an API, Amazon Bedrock, and an app, Anthropic’s Claude 3.5 is a large language model that can help businesses with advanced analytics, document processing, and highly articulate text generation that is well-written and friendly in tone. Notably, Claude 3.5 Sonnet is twice as fast as Claude 3 Opus and significantly more intelligent, especially in graduate-level reasoning.
Claude has been compared to GPT in terms of functionality, but it stands out in one major way: recall. Its context window (about 200,000 tokens) is larger than the average LLM, making it great for coders who want it to remember their previous exchanges, or an entire coding base, when it provides its new responses. This context window also has applications for businesses needing to summarize large documents, such as legal firms performing legal review.
Why I Picked It
Compared to other LLMs, Claude has an extremely large context window, which makes it a go-to option for professionals who need to summarize and analyze long files and documents. The LLM also happens to be a remarkably clear, coherent, and nuanced writer, capable of generating original human-like text in a conversational tone on a variety of topics.
And when it comes to prompting, in my experience the tool is often more capable of drawing inferences about what you want it to create, so you don’t have to be super precise, which can be difficult and time-consuming for those without prompt engineering expertise.
Pros and Cons
| Pros | Cons |
|---|---|
| Very conversational, friendly chatbot experience | Low request quote—about 45 messages per five hours |
| 200,000-token context window | Can struggle with math problem solving |
| Lighting-fast responses | Must pay to access important advanced features |
Pricing
- Free plan: Through Claude app (access to Claude 3.5 Sonnet)
- Pro: $20 per person per month (access to Claude 3 Opus and Claude Haiku, more usage, and early access to new features)
- Team: $25 per person per month (more usage than Pro)
- Enterprise: Must contact sales (more usage than Team, expanded context window, data source integrations, and more)
Features
- Text summarization
- Content generation
- Advanced reasoning
- Data analysis
- File uploading and tracking
- 200,000-token context window
- Friendly, relatable, accurate chatbot
Key Features of Large Language Model Software
Large language model software typically includes features that help businesses process large amounts of information and answer complex questions about their market or company data. LLMs also generate intelligent, contextually relevant outputs in various formats, from coding and images to human-like textual responses. Since LLMs are generally meant to be “built-on-top-of,” their APIs and ability to integrate with other applications are also massively important to users.
Conversational AI Chatbot
Most LLMs offer an AI chatbot, which understands and generates human-like responses based on user input and training data. These helpful chatbots continuously improve their performance—including their ability to follow your directions—by analyzing interactions and your satisfaction with them. Professionals generally use chatbots to quickly write content, conduct research, generate code, and analyze data.
Text Summarization
Text summarization is a powerful feature of LLMs that can save your business a lot of time when it comes to reading and interpreting lengthy documents, such as legal contracts or financial ledgers. AI-based text summarization works by condensing these swathes of text into concise representations while retaining the key information. Acting like an analyst, this feature can aid in decision-making by providing you with the most relevant details of long reports and studies. It can also help you create content based on the document, such as an abstract for a dense lab report.
Content Generation
Marketers and small business owners will probably find LLMs’ ability to generate content to be its most time-saving feature. Using specific prompts like “Write a witty social media caption to this image,” users can quickly pump out sophisticated and human-like content.
End results include email copy, social media posts, sales pages, product descriptions, and more. Of course, when writing with these tools, you should take care to add your own personality and insight into the copy, acting as its editor. Otherwise, the content might read as robotic and contain errors.
Fine-Tunability
Crucial for the applicability of LLMs, fine-tunability is the ability of LLMs to be customized to specific tasks or domain-specific knowledge with relatively small amounts of task-specific data.
For example, say a SaaS brand is using a customer chatbot powered by an LLM, and they notice the chatbot is struggling to answer questions about upgrade options for a specific product tier. The company then fine-tunes the LLM using a dataset containing transcripts of buyer interactions related to these specific upgrades, thus improving its performance.
Multimodality
In business, you often need to create more than just text. Multimodality refers to an LLM’s ability to understand and generate responses in other modalities such as code, images, audio, or video.
This opens up opportunities for businesses to create applications that leverage multiple modalities, such as augmented reality (AR) experiences or interactive multimedia content. It also helps businesses engage with customers—imagine a chatbot that can analyze a photo of a broken product and then recommend solutions and steps to fix it in image and text.
APIs & Third-Party Integrations
Third-party integrations and application programming interfaces are important features of LLMs because they enable seamless integration of language model capabilities into existing systems and applications, allowing businesses to leverage the power of natural language processing without having to develop their own models from scratch. To illustrate, businesses commonly integrate their LLM with their customer service platform to build smarter AI chatbots.
How to Choose the Best Large Language Model for Your Business
The best LLMs typically offer streamlined content generation, text summarization, data analysis, and third-party integrations while also being highly customizable and accurate. That said, the ideal large language model software for your business is one that aligns with your particular needs, budget, and resources.
Before evaluating the LLMs, you should also identify the use cases that matter most to you so you can then find models designed for those applications. Do you value affordability the most? Do you need a robust feature list and have the budget to deploy it? Given the complexity of LLMs—including how rapidly the sector changes—extensive research is always required.
How I Evaluated Large Language Models
To evaluate the best LLMs, I assessed their pricing, parameter size, context window, customization options, and overall deployability. Each percentage represents the importance of the factor to the typical business user.
Intelligent Outputs – 30 percent
To assess the intelligence of the large language models, I reviewed research comparing their scores on various intelligence tests in reasoning, creativity, analysis, math, and ability to follow instructions.
Cost – 20 percent
I scored each tool on pricing by evaluating their free versions and by finding the cost of their paid versions, in terms of computational resources and price.
Accuracy – 20 percent
To assess the accuracy of a tool’s output and question answering, I looked into the LLM’s parameter size, the quality of the training data, frequency of retuning, and various tests on accuracy.
Customization – 15 percent
To investigate the customization options of each LLM software, I looked at how well each model can be fine-tuned for specific tasks and knowledge bases and integrated into relevant business tools.
Context Window – 15 percent
The context window size determines the scope of information the model can consider when making predictions or generating text, making it a proxy for how well an LLM can understand linguistic patterns, produce contextually coherent outputs, and simulate real-world dialogue.
Frequently Asked Questions (FAQs)
The applications of large language models range from customer service chatbots and market research to document summarization and content creation in various formats, including text, images, and code.
The advantages of large language models in the workplace include greater operational efficiency, smarter AI-based applications, intelligent automation, and enhanced scalability of content generation and data analysis.
The major limitations and challenges of LLMs in a business setting include potential biases in generated content, difficulty in evaluating output accuracy, and resource intensiveness in training and deployment. Additionally, the need for robust security measures to prevent misuse is a major issue for companies.
The power of LLMs comes from their ability to leverage deep learning architectures to model intricate patterns in large datasets, enabling nuanced understanding and generation of language.
Bottom Line: The Power of Large Language Models
With the right large language model software, you can streamline many critical tasks for your business and free up more time to focus on strategic thinking and creative work. LLMs are the very foundation of success with artificial intelligence, and so selecting the best LLM for your purposes goes a long way toward gaining value from your AI use.
Despite GPT-4 winning in terms of public profile, the choices are numerous. There are many types of LLMs, each with unique features, powers, and limitations. It’s important to pick the tool that automates your most time-consuming tasks, integrates with your current tech stack, and helps your business achieve its goals, whether you want to increase marketing output or analyze data faster.
For a full portrait of the AI vendors and the wide array of LLMs they use, read our in-depth guide: 150+ Top AI Companies.