To pick the best LLM to create your chatbot, start from the job: define the chatbot's purpose and audience, compare models on accuracy, context handling, languages, speed and cost, then prototype two or three and test them with real users. Large language models (LLMs) are the most common type of text-handling AI. ChatGPT is the best-known example, but a wide range of chatbots and text generators use LLMs, including Google’s Gemini, Anthropic's Claude, Meta’s Llama3, and many more.
Some LLMs have been under development for years, while others emerged to ride the buzz cycle. This guide goes over the best-known LLMs you can use to create your chatbot, then the selection steps. The model details were written in mid-2024 (the newest release named is Claude 3.5 Sonnet, June 2024), and every vendor has shipped newer versions since, so check each vendor's model page before you build.
But first, what is an LLM?
A large language model (LLM) is a general-purpose AI text generator that powers most AI chatbots and writing generators. At heart, an LLM is a very capable auto-complete. Without sophisticated interfaces or other workarounds, they accept a prompt and construct an answer using a string of believable follow-on text.
The website 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 between models, and developers differ in how carefully they secure rights to their training material, but as a general rule you can assume the models were trained on a large share of the public internet and a great many published books.
This is why LLMs can produce relevant material on such a wide range of topics.
Top LLMs as of mid-2024
Now that you know what an LLM is and how it works, here are the most relevant LLM families.
1. OpenAI’s GPT
OpenAI's Generative Pre-trained Transformer (GPT) models have fueled the recent AI hype cycle. There were four primary variants as of mid-2024: GPT-3.5, GPT-3.5-turbo, GPT-4, and GPT-4 Turbo. There was also a newer multimodal version, GPT-4o.
· GPT-3.5: an improved version of GPT-3 that powered the original ChatGPT. It is faster than GPT-4 and suits most general and conversational tasks.
· GPT-3.5-turbo: a version of GPT-3.5 tuned for chat and faster responses across many topics.
· 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: a large multimodal model that accepts voice, images, and text and makes conversation in ChatGPT feel more natural. Users can speak to it as they would in a normal conversation, and the real-time interaction may also pick up on emotion. GPT-4o can view photos or screens and ask questions about them during a conversation. OpenAI says GPT-4o can answer audio in as little as 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 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 model in Google's Gemini lineup, and Google reported that it outperforms GPT-4 on various benchmarks. 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 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 and reported new benchmark highs on a variety of cognitive tasks. 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 latest addition to the Claude family at the time, in June 2024, was Claude 3.5 Sonnet.
· Claude 3 Opus: The most capable model of the Claude 3 family, Claude 3 Opus shows what Anthropic calls near-human understanding and fluency in complicated tasks. Anthropic positions it for complex tasks that depend on a strong grasp of context and user intent.
· Claude 3 Sonnet: balances intelligence and speed for business use. Anthropic says it is twice as fast as Claude 2 and Claude 2.1 for the vast majority of workloads while scoring higher.
· 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 answer simple questions and requests with very little delay.
· Claude 3.5 Sonnet: Anthropic says it handles subtlety, humor, and complicated instructions better than earlier versions and runs at double the speed of Claude 3 Opus.
Which model to choose from this family:
Go with Claude 3 Opus if you want a model with reliable 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 is one of the most popular and powerful open LLMs, with code available on GitHub.
As of mid-2024, Meta had released 8 billion and 70 billion parameter versions, with a 400 billion parameter version still in training. Meta's previous model family, Llama 2, was still available with 7 billion, 13 billion, and 70 billion parameter options.
Why choose Llama 3?
Llama 3 is open and handles a wide range of language tasks, so a team with engineers can run and tune it on its own infrastructure. The model itself is free to download, but running it still costs infrastructure and engineering time.
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. That makes it a fit for teams that want to add text processing to existing software.
Why choose Cohere?
Cohere suits teams that process a lot of text and want to plug a model into existing systems for analysis or content tasks.
6. Mistral
Mistral is a large language model that focuses on producing high-quality natural language understanding and generation. Mistral is built for a wide range of language tasks and can be adapted to specific industries and constraints.
Why choose Mistral?
Mistral suits teams that want a model they can adapt to their own requirements.
7. Falcon
Falcon is a 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 for its fast processing and its open-source accessibility: there are no per-use license fees.
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. Each has its own advantages and downsides, but the model is rarely what decides the result. The content behind the chatbot matters more.
For example, GPT-4 handles advanced coding and complicated reasoning well, and OpenAI reported that it hallucinates (gives fake or erroneous information) less than earlier GPT models. No model removes hallucination, which is why grounding answers in your own content matters more than the model you choose.
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?
Grounding: Can you confine it to answer only from your documents and cite them?
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.
Old Documents: Test with outdated pages in the mix. Wonderchat's support team sees one failure more often than any other, and it is not invention. It is retrieval pulling an archived policy and answering from it correctly: the citation is real and the answer is wrong.
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 developing fast and will most likely condense into a few large foundation models capable of a wide range of tasks. The difference between them will come mostly from how well they use the context they are given, and that context is the part you control.
So pick by constraint. Choose a hosted model (GPT, Claude, Gemini) if you want the least infrastructure; an open model (Llama, Mistral, Falcon) if your data must stay on your own servers; Cohere if the job is classification and extraction more than conversation.
If you would rather not build on a raw model at all, a no-code platform such as Wonderchat skips that step: its AI agents answer only from your own help center, docs and website, show the source on every reply, and say so when the content doesn't cover a question. The Free plan includes 50 AI replies a month, and paid plans for more AI replies are on Wonderchat's pricing page. The trade-off is control: a platform will not let you fine-tune or self-host the model the way an open-weights build does. For a founder or first support hire who only needs answers from a help center, that trade is usually fine: the model is a setting, and the content behind it matters more.
