6 Best Large Language Models (LLMs) in 2024 – eWeek

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Large language models and the AI tools they support have a remarkable ability to skyrocket operational efficiency for businesses of all sizes and industries.

Today’s best general-use large language models can streamline a large variety of business tasks, from content creation and customer service to data analysis and document processing.

Able to understand and generate human-like text, 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. LLMs can also be integrated into your current software platforms to improve their efficiency and effectiveness and open up new functionality and automations.

Here are our 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 2: Best for a free, resource-light, customizable LLM.
  • Cohere: Best enterprise LLM for building a company-wide search engine.
  • Gemini: Best for an AI assistant in Google Workspace.
  • Claude 3: Best for a large context window.

Top Large Language Model Software: Comparison

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 Starting Pricing
GPT-4 Open AI 1.7 trillion Chat GPT (uses 3.5, must upgrade for 4) and the Open AI API $20 per month for access to GPT-4
Falcon Technology Innovation Institute (TII) 180 billion Open source (available on Amazon SageMaker and Hugging Face) Free
Llama 2 Meta 70 billion Open source (download to desktop) Free
Cohere Cohere 52 billion Open source (Cohere API is the easiest access option) Free
Gemini Google Unrevealed Google Gemini App or Gemini API Free
Claude 3 Anthropic About 2 trillion Claude AI app and Claude API $0.25 per million input tokens and $1.25 per million output tokens

GPT-4: Best for Creating Marketing Content

OpenAI’s GPT-4, accessed typically through the free AI tool ChatGPT, is an advanced natural language processing model. 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 can 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.

Pros & Cons

Pros Cons
Free basic version with ChatGPT Occasional hallucinations
Can understand and create visual information Output is politically biased
Coherent, detailed text outputs Must buy subscription for advanced features


  • ChatGPT-3.5: Free version.
  • ChatGPT-4 Plus: $20 per month (create custom chatbots, access latest upgrades, image generation, and generally more intelligent responses).


  • Generate articulate, creative text
  • Edit and optimize copy
  • Summarize text and pictures
  • Conduct market analysis
  • Do keyword research
  • Write code
  • 175 billion 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 an open-source LLM that is tailored 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.

Pros & Cons

Pros Cons
Open to commercial and research use Fewer parameters than GPT
Highly conversational user experience Supports only a handful of languages
Human-like text generation Falcon 180B is resource-intensive to run


  • Falcon is a free AI tool and can be integrated into applications and end-user products.


  • 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

For more information about generative AI providers and their LLMs, read our in-depth guide: Generative AI Companies: Top 20 Leaders

Llama 2: Best for a Free, Resource-Light, Customizable LLM

Meta AI’s Llama 2 is an open-source large language model that can assist with a variety of business tasks, from generating content to training AI chatbots. Its three models are trained on a relatively small number of parameters, making them fast in terms of prompt processing and response time.

Designed to be fine-tuned using your company- and industry-specific data, users download it for free to their desktop and customize it to their 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.

Pros & Cons

Pros Cons
Fast and resource-efficient Before fine-tuning, no coding or math support
Free and open-source Output may not be as creative as GPT
High scores in reasoning and coding tests Smaller parameter size than comparable tools


  • Open source and free for research and commercial use.


  • Advanced reading comprehension
  • Text generation
  • Company-wide search engines
  • Text auto-completion
  • Data analysis

Cohere: Best Enterprise Solution for Building a Company-Wide Search Engine

Cohere is an open-source LLM 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.

Pros & Cons

Pros Cons
High-quality semantic analysis More expensive than most LLMs
Data and searches are kept private Free version is mostly a trial/testing ground
Highly customizable Ill-suited for smaller businesses and startups


  • 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.


  • Designed for enterprise applications
  • 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.

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. Starting at $20 per month, you can use Gemini to easily find and draft documents, analyze spreadsheet data, write personalized emails, build presentations and decks, and more.

Pros & Cons

Pros Cons
Highly affordable option for professionals Gemini Pro (free version) lacks accuracy
Connects seamlessly with Google apps Relatively new compared to other tools
Impressive reasoning capabilities Slightly glitchy photo interactions


  • 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, advanced Google Suite features, and functionality to do complex tasks).


  • Conversational AI chatbot
  • Create presentations easily
  • Generate content
  • Analyze reams of data
  • Multimodality
  • Google Workspace AI assistant

Claude 3: Best for a Large Context Window

Available through API and an app, Anthropic’s Claude 3 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.

It’s 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.

Pros & Cons

Pros Cons
Very conversational, friendly chatbot experience Low request quota—about 45 messages per five hours
200,000-token context window Cannot produce new images
Lightning-fast responses Can struggle with numerical and logic questions


  • Claude 3 comes in three different sizes: Haiku for simple tasks, Sonnet for difficult tasks, and Opus for highly complex tasks.
  • Pricing starts at Haiku: Input: $0.25 / MTok, Output: $1.25 / MTok.


  • Text summarization
  • Content generation
  • Advanced reasoning
  • Data analysis
  • File uploading and tracking
  • 200,000-token context window
  • Friendly, 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—aka 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.


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, pretend 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.


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 large language models 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 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 We Evaluated Large Language Models

To evaluate these large language model software, we 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%

To assess the intelligence of the large language models, we reviewed research comparing their scores on various intelligence tests in reasoning, creativity, analysis, math, and ability to follow instructions.

Cost – 20%

We 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%

To assess the accuracy of a tool’s output and question answering, we looked into the LLM’s parameter size, the quality of the training data, frequency of retuning, and various tests on accuracy.

Customization – 15%

To investigate the customization options of each LLM software, we 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%

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): Large Language Models

What are the applications of large language models?

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.

What are the advantages of using large language models?

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.

Are there any limitations or challenges with large language models?

The major limitations and challenges of LLMs in a business setting include potential biases in generated content, difficulty in evaluating output accuracy, resource intensiveness in training and deployment. Additionally, the need for robust security measures to prevent misuse is a major issue for companies.

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. 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 2024

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