Best (Large Language Models) LLMs in 2024 & Beyond

About The Author

Nikhil-KhandelwalNikhil Khandelwal VP Engineering
LinkedIn|8 Aug 2024

Artificial intelligence has grown tremendously recently, particularly in large language models (LLMs). The LLM market is set to Reach USD 6.5 Billion by 2024 and USD 140.8 billion by 2033.  

These advanced models drive significant changes across various industries, transforming how businesses interact with customers, analyze data, and automate processes. 

If you want to build your own LLM models or AI chatbots, it's crucial to stay updated on the best LLMs available in 2024 and beyond. This blog will help you understand the best LLM software, why it’s worth your attention, and how it can benefit your business. 

What are the Best Large Language Models in 2024 & Beyond? 

There are dozens of LLMs available in the market, and they’re becoming popular because of their capabilities, performance, and applications across various industries.  

Here is a list of the top 11 large language models (LLMs) for your business: 

  1. GPT 
  2. Gemini 
  3. Claude 3 
  4. Llama 3 
  5. LaMDA 
  6. Gemma 
  7. BERT 
  8. PaLM 
  9. Falcon 
  10. Cohere 
  11. Vicuna 

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Top 11 Large Language Models At A Glance 

Best LLMs 

Developer 

Best For 

Parameters 

Accessibility 

Pricing 

GPT 

Open-AI 

Creating marketing content 

175 billion 

API 

$0.02 per 1k tokens 

 

Gemini 

Google DeepMind 

An AI assistant in Google Workspace 

130 billion 

API 

Custom Pricing 

Claude 3 

Anthropic 

A large context window 

150 billion 

API 

Custom Pricing 

Llama 3 

Meta 

A free, resource-light,customizable LLM 

200 billion 

Open 

Custom Pricing 

LaMDA 

Google 

Natural conversations and dialogue systems 

137 billion 

API 

Custom Pricing 

Gemma 

Microsoft 

Context-aware applications and personalized responses 

175 billion 

Open 

Custom Pricing 

BERT 

Google 

Text classification, sentiment analysis, and question answering 

345 billion 

Open 

Free 

PaLM 

Google 

High-level reasoning, multilingual tasks, and complex queries 

540 billion 

API 

Custom Pricing 

Falcon 

TII UAE 

A human-like, conversational chatbot 

40 billion 

Open 

Custom Pricing 

Cohere 

Cohere 

Building a company-wide search engine 

52 billion 

API 

Custom Pricing 

Vicuna 

UC Berkeley 

Research and academic purposes, open-source projects 

13 billion 

Open 

Free 

 

Best LLM for Creating Marketing Content 

GPT

GPT

OpenAI’s GPT is a free AI tool. ChatGPT is a highly interactive chatbot and advanced natural language processing (NLP) model. Four main models (GPT-3.5-turbo, GPT-4, and GPT-4 Turbo) and one multimodal version (GPT-4o) are available.  

Large-scale pretraining, contextual understanding, fine-tuning capabilities, and advanced architecture make ChatGPT a great assistant to any marketer.  

Pros & Cons of GPT: 

Pros 

Cons 

Highly versatile 

Often provides misleading data 

Easy to integrate via API 

Lack real-time awareness 

Supports multiple languages 

May struggle with highly technical and specialized queries 

Standout Features of ChatGPT: 

  • Automate content creation, translation, idea generation, coding, and customer support 
  • Process both text and image inputs simultaneously  
  • Cross-platform compatibility and API integration  

Pricing: 

  • ChatGPT-3.5: Free version. 
  • ChatGPT-4 Plus: $20 per month 
  • Create custom chatbots 
  • Access latest upgrades 
  • Image generation 
  • Generally, more intelligent responses 

Rating & Reviews of ChatGPT: 

  • G2: 4.5/5 (30+ reviews) 
  • Capterra: 4.7/5 (15+ reviews) 

Best LLM for an AI assistant in Google Workspace 

Gemini

Gemini

Gemini AI (formerly Bard), by Google, is a large language model, content generator, and AI chatbot that handles various complex NLP tasks. It’s known for versatility and high performance as it aims to provide advanced AI capabilities across different domains.  

This free AI tool is known for real-time applications and for providing quick responses for customer service bots.  

Pros & Cons of Gemini:

Pros 

Cons 

Highly affordable option for professionals 

Slightly glitchy photo interactions 

Seamlessly integration with Google apps  

Free version has lack accuracy 

Impressive reasoning capabilities 

 

Standout Features of Gemini: 

  • Combines vision and language capabilities 
  • Provides enriched and contextually aware responses 
  • Advancements in NLP and computer vision 

Pricing: 

  • Free  
  • Pay-as-you-go: Input: $7/1M Tokens; Output: $21/1M Tokens 

Rating & Reviews: 

  • G2: 4.5/5 (100+ reviews) 

Best LLM for a Large Context Window 

Claude 3

Claude 3

Claude 3, a version of Claude, is an innovative large language model. Its three models - Haiku, Sonnet, and Opus - are safe for enterprise customers. Claude's aptitude for delivering contextually appropriate responses makes it ideal for training conversational AI applications. 

This LLM model can perform advanced reasoning tasks beyond pattern recognition or text generation. It can transcribe and analyze handwritten notes, photos, and static images. In addition, it also supports code generation and multilingual processing.  

Want personalized interactions, imporved performance, more natural conversion and cost-effective solution? Build an LLM RAG chatbot 

Pros & Cons of Claude 3: 

Pros 

Cons 

Friendly chatbot experience 

Can struggle with numerical and logic questions 

200,000-token context window 

Limited Availability 

Lightning-fast responses 

Complex 

Standout Features of Claude 3: 

  • Use Claude 3 to process roughly 30 pages of text/second. 
  • Easily integrate Claude into your existing tech stack without deep technical expertise. 
  • Conversational AI for consistent tone and style in customer interactions. 
  • Extract information from business emails or conclude survey responses. 

Pricing: 

  • Haiku: Input: $0.25 / MTok, Output: $1.25 / MTok. 
  • Business and Scale: Custom pricing 
  • Free 
  • Pro: $20 per person per month 
  • Team: $30 per person per month (minimum five people) 

Rating & Reviews: 

  • G2: 4.7/5 (20+ reviews) 
  • Capterra: 4.8/5 (4 reviews) 

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Best LLM for a Free, Customizable  

Llama 3

Llama

Llama, aka Large Language Model Meta AI, is designed to facilitate innovation for developers and researchers. The first version of Llama was launched in February 2023.  

One important advantage of Llama 3 is that it is open-source. This means developers can easily fine-tune it and create new AI models to cater to specific tasks. In addition, this software can create codes and natural language about code from prompts.  

Pros & Cons of Llama 3: 

Pros 

Cons 

Fast and resource-efficient  

Output may not be more creative than GPT 

Free and open-source  

Smaller parameter size 

High scores in reasoning and coding tests  

 

Standout Features of Llama 3: 

  • Perform NLP tasks like content generation, summarization, and translation 
  • Designed for developers, researchers, and businesses  
  • Generate code and natural language prompts 

Pricing: 

  • Free or Open Access for Researchers  

Rating & Reviews:  

Capterra: 4.0/5 (1 review) 

Best LLM for Natural conversations and dialogue systems 

LaMDA

LaMDA

LaMDA (Language Model for Dialogue Applications), developed by Google Brain by utilizing decoder-only transformer language model. 

LaMDA's potential use cases are diverse, encompassing customer service, chatbots, personal assistants, and more. It is built on the foundation of an earlier Google chatbot called Meena. The conversational service powered by LaMDA is known as BARD.

Pros & Cons of LaMDA: 

Pros 

Cons 

Highly versatile 

Privacy concerns 

Suitable for high-traffic applications 

Low accessibility 

Seamlessly integration with Google services 

Bias and Fairness 

Standout Features of LaMDA: 

  • Pre-trained on a vast and diverse text corpus 
  • Create natural, human-like conversations 
  • Built on previous models like Meena 
  • Multimodal capabilities and adaptive learning 

Best LLM for Context-aware applications and personalized responses 

Gemma

Gemma

Google Gemma is a family of open AI models developed by Google. It is built on the same research and technology that created Gemini. It is available in two sizes: 2 billion parameters and 7 billion parameters. 

Pros & Cons of Gemma: 

Pros 

Cons 

Advanced Technology 

Complexity 

Open AI Model 

Privacy Concerns 

Highly Scalable 

Resource Intensive 

Standout Features of Gemma: 

  • Built on the same cutting-edge research and AI technology 
  • Available in two sizes (2 billion and 7 billion parameters) 
  • Capable of handling complex tasks and delivering high-quality outputs 
  • Backed by Google's extensive resources and expertise 

Best LLM for Text Classification, Sentiment Analysis, and Question Answering 

BERT

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a Google-built machine learning model for NLP (Natural Language Processing). It’s a transformer-based language model known for its ability to understand context in natural language processing tasks.  

It is a bidirectional (can analyze text from both left and right) and unsupervised language representation algorithm capable of analyzing large datasets and quickly training machine learning models. 

BERT is available in two main versions: base and large. While they share the same architecture, the critical difference is the number of parameters they utilize. Specifically, BERT large has 3.09 times more parameters for fine-tuning than BERT base. 

Pros & Cons of BERT:

Pros 

Cons 

State-of-the-Art Performance 

Large Model Size 

Robustness 

Computational Resources 

Pre-training and Fine-tuning 

Time-consuming and resource-intensive 

Standout Features of BERT: 

  • Train the machine learning model as per your text data 
  • Perform versatile tasks with BERT 
  • Multilingual version supports 104 languages 

Pricing: 

Open-source and freely available under the Apache 2.0 license.  

Best LLM for High-Level Reasoning, Multilingual Tasks, and Complex Queries 

PaLM

PaLM

PaLM (Pathways Language Model), developed by Google, represents a significant advancement in AI and natural language processing. It is trained on various datasets and excels at handling complex reasoning tasks such as coding, classification, and translation.  

The enhanced version, PaLM 2, can be utilized for research purposes and seamlessly integrated into product applications. 

Pros & Cons of PaLM:

Pros 

Cons 

High Performance 

Complexity 

Multi-Task Learning 

Accessibility 

Versatile 

Complexity 

Standout Features of PaLM: 

Achieve greater precision in various tasks with advanced language comprehension capabilities.  

Scale flexibly and efficiently using PaLM without requiring task-specific models.  

Simplify operations and handle multiple tasks concurrently with PaLM’s unified model.  

Utilize exceptional reasoning skills for scenarios that demand logical deduction, problem-solving, and decision-making. 

Pricing: 

Custom Pricing 

Best LLM for a Human-Like, Conversational Chatbot 

Falcon

Falcon 

Falcon, developed by the Technology Innovation Institute, is a sophisticated language model designed for complex natural language processing tasks. It has been trained on 40 billion parameters, one trillion tokens, and up to 180 billion parameters. It includes cutting-edge advancements in AI to boost both language understanding and generation capabilities. 

Released under the permissive Apache 2.0 license, Falcon is well-suited for both commercial and research applications. In short, Falcon is ideal for AI-enabled chatbots for customer management and virtual AI assistants that provide a more engaging, human-like experience than ChatGPT. 

Pros & Cons of Falcon: 

Pros 

Cons 

Open to commercial and research use  

Supports only a handful of languages 

Highly conversational user experience  

Resource-intensive to run 

Human-like text generation  

 

Standout Features of Falcon: 

  • Create human-like textual responses 
  • Track the context of the ongoing conversation 
  • Fine-tunable base model 
  • Answer complex questions 
  • Summarize information 
  • Integrate it at no cost into your business applications. 

Best LLM for Building a company-wide search engine 

Cohere

 Cohere

Like Claude 3, Cohere's Coral LLM is tailored for enterprise users. It provides an API and enables organizations to train custom versions of the model on their proprietary data. It allows for precise responses to specific queries from employees and customers. 

Cohere is an enterprise AI platform that helps businesses incorporate Generative AI into their daily operations, including document search, discovery, and retrieval in over 100 languages. It supports organizations in evolving their Generative AI models from proof of concept to total production. It facilitates scalable and efficient machine learning and AI app development

Pros & Cons of Cohere: 

Pros 

Cons 

High-quality semantic analysis  

Expensive 

Privacy & data security 

Ideal for small businesses or startups 

Highly customizable 

 

Standout Features of Cohere: 

  • User-friendly API makes it accessible for newbie technical expertise 
  • Excellent scalability 
  • Fine-tune models on data 
  • Provides transparency in how its models are trained 

Pricing: 

Free 

Default model 

  • Command R+: Input: $3/1M Tokens; Output: $15/1M Tokens 
  • Command R:  Input: $0.5/1M Tokens; Output: $1.5/1M Tokens 

Fine-tuned model 

  • Command R:  Input: $2/1M Tokens; Output: $4/1M Tokens; Training: $8/1M Tokens

Best LLM for Research and Academic Purposes, Open-Source Projects 

Vicuna

Vicuna

Vicuna is an open-source chatbot based on Meta's Llama LLM. It is widely utilized in AI research and is a critical component of Chatbot Arena, a benchmark platform LMSYS manages. 

Primarily, Vicuna is designed for research on large language models (LLMs) and chatbots, targeting researchers and enthusiasts in Natural Language Processing, Artificial Intelligence, and Machine Learning.  

Notably, preliminary evaluations revealed that Vicuna achieved over 90% of the quality of OpenAI's ChatGPT and Google Bard, consistently outperforming other models like LLaMA and Alpaca in over 90% of comparisons. 

Pros & Cons of Vicuna: 

Pros 

Cons 

Open-source 

Complex setup 

Customizable 

Security risks 

Strong community support 

Integration issues 

Standout Features of Vicuna: 

  • Offers a modular design that supports various extensions and plugins 
  • Reduces licensing costs, making it a cost-effective solution for many users. 
  • Provides transparency in its codebase 
  • Capable of scaling to meet different demands, from small projects to large systems.

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How to Choose the Best LLM Software for Your Business? 

Choosing the ideal large language model (LLM) for your business depends on your needs, budget, and available resources. Identify your critical use cases to find models tailored to those applications. 

  • Is cost your primary concern? 
  • Do you need a model with advanced features and have the budget to support it? 
  • Given the evolving nature of LLM technology, thorough research is essential. 

What Should You Look for In The Best LLMs? 

Here are five main factors you need to consider when finding the best AI tool or LLM software for your business:

What Should You Look for In The Best LLMs

  • High-Quality Outputs: Ensure the AI tool generates outputs that meet your quality standards. 
  • Accuracy: Look for a model that provides precise and reliable results. 
  • Cost-Effectiveness: Evaluate whether the tool offers good value for its price. 
  • Customization: Check if the tool can be tailored to fit your specific needs. 
  • Context Window: Assess the model’s ability to handle and remember context effectively. 

Key Features of Large Language Model Software 

Here are five key features of LLM software: 

  • Conversational AI Chatbot: This type of chatbot provides intelligent and context-aware responses in real-time conversations and can understand and generate human-like text. 
  • Text Summarization: Condenses lengthy documents or articles into concise summaries while retaining essential information and context. 
  • Versatile Task Generation: Can perform various tasks, including content creation, data analysis, and generating code snippets based on specific prompts. 
  • Fine-tunability: Allows customization and training on specific datasets to enhance performance for specialized applications or industry-specific needs. 
  • Multimodality: It supports the integration of multiple types of data, such as text, images, and audio, enabling more comprehensive interactions and outputs. 
  • APIs & Third-party Integrations: Provides flexible APIs for seamless integration with other software and systems, facilitating the embedding of LLM capabilities into diverse applications. 

These features make LLM software a powerful tool for various applications, from enhancing customer service with chatbots to supporting complex data analysis tasks. 

From GPT-4's versatility to Google Bard's integration capabilities, the suitable model can significantly enhance your AI-driven projects. By understanding the strengths and applications of these leading LLMs, you can make an informed decision to drive innovation and efficiency in your business. 

Planning to Build Your Own LLM Software Like ChatGPT? 

As you consider leveraging the power of LLMs for your business, it's crucial to partner with experts who can guide you through the development process. At VLink, we specialize in building cutting-edge LLM software and AI chatbots customized to your requirements.  

Our team of skilled LLM developers is ready to assist you in creating innovative solutions that drive efficiency and engagement. 

Ready to transform your business with advanced LLM technology? Contact us today to hire top-tier LLM developers who can bring your AI vision to life.  

FAQs
Frequently Asked Questions
What are Large Language Models?

Large Language Models (LLMs) are advanced AI systems trained on vast datasets to understand and generate human-like text. They enable applications such as chatbots, content creation, and data analysis.

How Does Large Language Models Work?

Large Language Models (LLMs) leverage deep learning techniques, particularly neural networks, to analyze and generate human-like text. They are trained on extensive datasets, learning patterns, context, and nuances of language. LLMs can understand context, predict text, and generate coherent responses by processing vast amounts of data.

What is the Future of LLMs?

The future of LLMs includes enhanced language understanding, real-time processing, and broader applications across industries. Innovations will focus on improving accuracy, efficiency, and ethical considerations, leading to more sophisticated AI-driven solutions in communication, automation, and decision-making.

What is the cost of building LLMs?

Building LLMs can cost between $1 million and $10 million, factoring in data acquisition, computational resources, infrastructure, and specialized talent for model development, training, and deployment.

Where to hire the top 3% of LLM developers?

To hire the top 3% of LLM developers, contact VLink, a reliable IT staffing agency. We rigorously vet candidates, ensuring access to highly skilled professionals.

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