Large language models (LLMs) are the most common type of text-handling AI, and they're showing up everywhere, one most known example of such models is ChatGPT. However, it is not the only one. A wide range of chatbots and text generators use LLMs. These include Google’s Gemini, Anthropic's Claude, Meta’s Llama3, and many more.

Some LLMs have been under development for years, while others have swiftly emerged to capitalize on the new buzz cycle. So here, we’ll be going over some of the best LLMs on the market right now, which you can use to create your chatbot.

But first, what is an LLM?

A large language model (LLM) is a general-purpose AI text generator that powers all AI chatbots and writing generators. LLMs have supercharged auto-complete capabilities. Without sophisticated interfaces or other workarounds, they accept a prompt and construct an answer using a string of believable follow-on text.

The chatbots built on top of LLMs aren't looking for keywords to respond with a scripted response; instead, they're attempting to comprehend what's being asked and responding properly. This is why LLMs have grown in popularity: the same models (with or without further training) can be used to answer customer questions, create marketing materials, summarize meeting notes, and execute a variety of other tasks.

How do LLMs work?

LLMs are trained on massive amounts of data. The specifics vary slightly between the various LLMs—depending on how careful the developers are to fully acquire the rights to the materials they're using—but, as a general rule, you can assume that they've been trained on at least the entire public internet and every major book that's ever been published.

This is why LLMs can produce relevant material on such a wide range of topics.

Top Current LLMs

Now that you know what an LLM is and how it works, let’s examine some of the most relevant LLMs today.

1.      OpenAI’s GPT

OpenAI's Generative Pre-trained Transformer (GPT) models have fueled the recent AI hype cycle. There are four primary variants available right now: GPT-3.5, GPT-3.5-turbo, GPT-4, and GPT-4 Turbo. There is also a new multimodal version, GPT-4o.

·         GPT-3.5: GPT-3.5 is an improved version of GPT-3 that has fewer parameters and powers  ChatGPT. Faster than GPT-4 and more adaptable than GPT Base, this model series is suitable for most activities, whether conversational or general.

·         GPT-3.5-turbo: GPT-3.5 Turbo is a flexible and resilient big language model noted for its extensive knowledge and versatility across several domains.

·         GPT-4: GPT-4 is a very advanced iteration of the Generative Pre-trained Transformer series, with considerable advancements in comprehending and producing nuanced, contextually rich text.

·         GPT-4 Turbo: GPT-4 Turbo expands on GPT-4's capabilities by increasing speed and efficiency while maintaining the depth and accuracy of its replies.

·         GPT-4o: GPT-4o provides a more realistic human interaction for ChatGPT and is a huge multimodal model that accepts a variety of inputs like as voice, images, and text. The discussions allow users to respond as they would in a typical human conversation, and the real-time interaction may also detect emotions. GPT-4o can view photographs or screens and ask inquiries about them during interactions. GPT-4o can answer in 232 milliseconds, which is comparable to human response speed and quicker than GPT-4 Turbo.

Reasons to pick each model.

·         Go with GPT-3.5 if you need a cost-effective option with solid performance for most general-purpose tasks.

·         Choose GPT-3.5-turbo if you want a faster response time compared to GPT-3.5 while maintaining similar performance.

·         Pick GPT-4 if you require advanced capabilities and higher accuracy for complex interactions and nuanced understanding.

·         GPT-4-turbo if you want the high performance of GPT-4 but with faster response times and better efficiency.

·         Go with GPT-4o if you need the latest generation's cutting-edge features and improvements, aiming for state-of-the-art performance and versatility.

2.      Google’s Gemini

Gemini is a family of LLMs developed by Google to power the company's chatbot of the same name. The model replaced PaLM in powering the chatbot, which was renamed from Bard to Gemini after the model transition. It comes in three sizes: ultra, pro, and nano.

·         Gemini Ultra: Ultra is the largest and most powerful model of Google's Gemini lineup. It outperforms GPT-4 on various metrics and has remarkable capabilities. This version can swiftly grasp and analyze information in various formats, including words, graphics, audio, and video.

·         Gemini Pro: Pro is the mid-tier model that strikes a mix between efficiency and capability, making it ideal for a variety of applications.

·         Gemini Nano: Gemini Nano is a lighter and more efficient version of Google Gemini. It is excellent for situations requiring maximum efficiency and resource optimization.

The decision between choosing Gemini Ultra, Gemini Pro, and Gemini Nano depends on your specific use case.

·         If you want a very powerful model capable of processing complicated data across several modalities, Gemini Ultra is an excellent solution.

·         For AI-powered conversational experiences, Gemini Pro provides a balanced solution.

·         If your device's resources are restricted, Gemini Nano delivers lightweight and efficient performance.

3.      Anthropic's Claude

Anthropic introduced the Claude 3 model family in March 2024, setting new industry benchmarks on a variety of cognitive activities. The Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus are the three most advanced versions in the series, in order of capacity. The most recent addition to the Claude family, in June 2024, is Claude 3.5 Sonnet.

·         Claude 3 Opus: The most powerful model of the Claude 3 family, Claude 3 Opus demonstrates near-human understanding and fluency in complicated activities. Its superior knowledge of context and user purpose enables more natural and productive interactions, making it a fantastic tool for improving customer engagement and operational efficiency.

·         Claude 3 Sonnet: Claude 3 Sonnet provides the optimal blend of intelligence and speed for corporate applications. It is twice as fast as Claude 2 and Claude 2.1 for the vast majority of workloads while offering higher degrees of intelligence.

·         Claude 3 Haiku: The Claude 3 Haiku, the family's quickest and most compact variant, is developed to provide near-instant reaction. It is meant to respond to simple inquiries and requests with unprecedented speed, allowing users to create seamless AI experiences that resemble human interactions.

·         Claude 3.5 Sonnet: It recognizes subtlety, humor, and complicated instructions better than the previous versions in its model family and runs at double the speed of the Claude 3 Opus.

Which model to choose from this family model you ask? Well

• Go with Claude 3 Opus if you want a model with robust language comprehension and versatility in handling diverse conversational contexts.

• Choose Claude 3 Sonnet if you need a model optimized for producing high-quality, coherent responses in multi-turn conversations with an emphasis on maintaining context.

• Opt for Claude 3 Haiku if you prefer a lightweight model designed for quick response times and efficiency, suitable for applications with high traffic and lower resource availability.

• Choose Claude 3.5 Sonnet if you seek the latest advancements in language modeling. It offers superior performance and accuracy for complex and nuanced interactions.

4.      Meta’s Llama3

Llama 3 is a family of open LLMs developed by Meta, the parent company of Facebook and Instagram. It powers the majority of AI capabilities in Meta's products and is one of the most popular and powerful open LLMs, with the source code available on GitHub.

There are now 8 billion and 70 billion parameter versions available, with a 400 billion parameter version still being trained. Meta's previous model family, Llama 2, is still available with 7 billion, 13 billion, and 70 billion parameter options.

Why choose Llama 3?

Llama 3’s open-source methodology, paired with its capacity to perform a wide range of linguistic tasks, makes it an invaluable tool for businesses trying to improve their operations with artificial intelligence. Its free use enables organizations to invest more in other areas while still benefiting from cutting-edge technical solutions.

5.      Cohere

Cohere is a flexible LLM intended for a variety of text-based applications. It stands out for its simplicity of integration into existing systems as well as its solid performance in a variety of tasks such as text categorization, creation, and extraction. Cohere's versatility makes it a good alternative for firms wishing to use AI to improve their text-processing skills.

Why choose Cohere?

Cohere's features make it an invaluable tool for any organization that handles a significant volume of text and requires adaptive, fast AI solutions for text analysis and content development. Its integration may considerably improve efficiency and streamline processes across different departments.

6.      Mistral

Mistral is a cutting-edge large language model that focuses on producing high-quality natural language understanding and generation. Mistral is designed to perform a wide range of complicated language activities, and it excels at adapting to individual industry demands and constraints, making it ideal for bespoke applications.

Why choose Mistral?

Mistral's capacity to learn and provide context-specific replies makes it a great tool for organizations seeking to improve their operations using AI suited to their unique requirements. Mistal can help businesses achieve higher precision and efficiency in language-driven processes.

7.      Falcon

Falcon is a cutting-edge big language model noted for its speed and accuracy in text processing and generation. Falcon, being an open-source LLM, is well-suited for high-performance applications that need quick response times and dependability.

Why choose Falcon?

Falcon stands out not just for its fast processing capabilities but also for its open-source accessibility, allowing firms to function at market speed without incurring additional credit costs. Its integration with your systems has the potential to greatly improve operational efficiency, customer relations, and market response.

How to Select the Right LLM for Your Business and Goals

Gpt-3.5, Gpt-4, Gemini, Claude, and Llama3 are some of the most widely used LLMs these days. Each has its unique advantages and downsides.

For example, Gpt-4 is capable of advanced coding, complicated reasoning comprehension, and talents comparable to human competence and experience. It's also worth noting that Gpt-4 is one of the few models capable of greatly reducing hallucinations (providing fake or erroneous information). 

However, before finalizing a model, check for the following:

1.      Understand Your Requirements

It’s crucial to define your chatbot's requirements clearly. Consider the following factors:

• Purpose: Is your chatbot for customer support, sales, entertainment, or another use case?

• Audience: Who will be interacting with your chatbot? Are they tech-savvy or general users?

• Complexity: Do you need simple interactions or complex, multi-turn conversations?

• Language and Tone: What kind of language and tone should the chatbot use?

2.      Evaluate Model Capabilities

Different LLMs come with varied capabilities. Evaluate these based on your requirements:

• Accuracy and Comprehension: How well does the model understand and generate relevant responses?

• Multilingual Support: If your audience is global, you might need a model that supports multiple languages.

• Contextual Awareness: How well does the model handle context over multiple turns of conversation?

• Customizability: Can you fine-tune the model to suit your specific needs better?

3.      Consider Performance and Scalability

Performance and scalability are critical, especially for chatbots expected to handle high traffic:

• Response Time: How quickly does the model generate responses?

• Scalability: Can the model handle increasing loads without significant drops in performance?

• Resource Requirements: What are the hardware and software requirements for running the model?

4.      Evaluate Cost

Cost is a significant factor, particularly if your chatbot is expected to operate at scale:

• Licensing Fees: What are the costs associated with using the model?

• Operational Costs: Consider the costs of the infrastructure needed to run the model.

• Fine-tuning and Maintenance Costs: If customization is needed, factor in these additional costs.

5.      Test and Iterate

Once you have shortlisted potential models, it’s time to test them:

• Prototyping: Develop prototypes using the different models to see how they perform in real-world scenarios.

• User Testing: Get feedback from actual users to understand the strengths and weaknesses of each model.

• Performance Metrics: Measure the models using key performance indicators (KPIs) such as accuracy, user satisfaction, and response time.

6.      Make Your Decision

Based on your testing and evaluation, choose the model that best aligns with your requirements, budget, and user expectations. Remember, the ideal LLM should offer a balance between performance, cost, and scalability.

Takeaway

LLMs are fast developing and will most likely condense into a few large fundamental models capable of performing a wide range of tasks. We believe that distinction will be driven mostly by LLMs' ability to integrate and exploit contextual data.

Choosing the appropriate LLM might have a significant impact on your initiatives. Whether it's powering a chatbot or creating realistic audio messages, we hope we've provided you with information on some tools to help you make this decision!