Selecting Between AI models for your chatbots
A guide to decide how you can select the best AI model out of the 10 different AI model selections to power your AI chatbot.
Last updated
A guide to decide how you can select the best AI model out of the 10 different AI model selections to power your AI chatbot.
Last updated
We support a number of different models provided by multiple AI service providers, namely OpenAI, Anthropic, Minstral, Llama, Cohere, Gemini and many more, each with their own series of models. Each model has its own unique specifications and use cases, and in this guide we will explain how to choose the best model for your use case.
Choosing the right OpenAI model is crucial for optimizing your chatbot’s performance. OpenAI offers models with varying capabilities, from basic text generation to advanced language understanding and dynamic conversations. This guide will help you understand the key differences in performance, token limits, and use cases, so you can select the best model for your needs—whether it's for simple queries or complex tasks.
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*GPT-4o mini 60k context is an alternative model to the GPT-4mini model provided that provides for better quality answers due to a larger context window included
If you are working on a budget or with limited resources, GPT-4mini is a suitable choice
If your application doesn’t require extensive context memory or complex function calling, GPT-4o will serve you well
If you require your AI to have a large context window because you have a long custom instruction for your chatbot, GPT-4 Turbo (40k) is a good choice.
If you need your chatbot to constantly cite accurate links or for your chatbot to do advanced maths or calculation, GPT-4 or at least the GPT-4 Turbo model is the way to go
Anthropic has released several models of its Large Language Model series, each with its own unique specifications and use cases. In this document, we will discuss the most optimal use cases for these models.
The difference between Anthropic AI models lies in their capabilities and pricing tiers. Anthropic's new family of AI models, Claude 3, consists of three models: Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku
In summary, Claude is a popular alternative to OpenAI's GPT models, as some prefer the LLM for its friendlier tone. For others, Claude's AI models work better with their knowledge bases than OpenAI's.
If you are working on a budget or with limited resources, Claude Haiku is a suitable choice
If your application requires extensive context memory, try the Claude Haiku 60K model.
If you require your AI to have a large context window because you have a long custom instruction for your chatbot, Claude Sonnet (5k) is a good choice.
If you need your chatbot to constantly cite accurate links or for your chatbot to do advanced maths or calculation, Claude Opus or at least the Claude Sonnet model is the way to go
Selecting the right Gemini model for your chatbot is crucial to ensure it meets your performance needs. Gemini 1.5 Flash is designed for faster response times, making it ideal for basic interactions with minimal latency. On the other hand, Gemini 1.5 Pro offers real-time information retrieval, making it more suitable for tasks that require up-to-date and accurate data. This guide will walk you through the key differences between these models, helping you choose the best option for your chatbot’s requirements.
We will be continually updating this page as we introduce more and more AI models. If you would like to request for a custom AI model to be made available on Wonderchat, you can reach out to provide us feedback here.
If you have any more questions, feel free to reach out to us at support@wonderchat.io
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Description
The original GPT-4 model is a large multimodal model that accepts both text and image inputs, providing advanced reasoning and problem-solving capabilities.
Costs
20 messages per user query and chat response
Strengths
Exceptional at complex language tasks, capable of generating coherent and contextually relevant text. It offers high accuracy and is optimized for chat applications.
Use Cases
Suitable for applications requiring deep understanding and generation of text, such as customer support, content creation, and educational tools.
Description
A variant of GPT-4 that is optimized for speed and efficiency while maintaining high performance.
Costs
10 messages per user query and chat response
Image Reading Capabilities
Available
Strengths
Faster response times compared to the standard GPT-4, making it ideal for real-time applications. Supports vision capabilities and function calling.
Weaknesses
More expensive than the GPT-4o models due to higher intelligence and resources required
Use Cases
Best for chatbots and applications needing quick interactions, such as virtual assistants and interactive games.
Description
The "omni" model that is multimodal, accepting both text and image inputs, designed for a broader range of tasks.
Costs
5 messages per user query and chat response
Image Reading Capabilities
Available
Strengths
Higher intelligence than previous models, cost-effective, and capable of handling complex tasks efficiently.
Weaknesses
Complexity in use, higher risk of toxic output, potential inconsistencies in response quality
Use Cases
Ideal for applications that require both text and image processing, such as content creation, data analysis, and customer service.
Description
A smaller and more affordable version of GPT-4o, optimized for lightweight tasks while still offering advanced capabilities.
Costs
1 messages per user query and chat response
Strengths
Cost-effective, fast, and capable of handling various tasks.
Weaknesses
Reduced performance on complex tasks, limited functionality compared to larger models, susceptibility to errors
Use Cases
Suitable for smaller applications, such as chatbots, virtual assistants, and basic content generation where speed and cost are priorities.
Description
Exceptional performance on highly complex tasks with near-human fluency and understanding
Costs
20 messages per user query and chat response
Strengths
Exceptional performance on highly complex tasks with near-human fluency and understanding
Weaknesses
Most expensive model in the Claude series
Use Cases
Advanced research tool to look at complex research documents. As a critical analysis assistant for a corpus of reports.
Description
Exceptional performance on highly complex tasks with near-human fluency and understanding
Costs
20 messages per user query and chat response
Strengths
Exceptional performance on highly complex tasks with near-human fluency and understanding
Weaknesses
Most expensive model in the Claude series
Use Cases
Advanced research tool to look at complex research documents. As a critical analysis assistant for a corpus of reports.
Description
Fastest and cheapest model for near-instant responsiveness. Answers simple queries and requests with unmatched speed. Suitable alternative to the GPT-4mini model.
Costs
1 messages per user query and chat response
Strengths
Most cost-effective model in its intelligence category
Weaknesses
Limited to simple queries, may not handle complex tasks as well as Sonnet or Opus. May not be as fluent in handling multilingual chats.
Use Cases
FAQ bots trained on a simple knowledge base that provide quick answers to common questions without needing in-depth analysis or a nuanced understanding of the user’s query.
Description
AI model designed by Google with real time information access.
Costs
5 messages per user query and chat response
Strengths
Can access up to date information from the web, however may have limited functionality and adherence to base prompts as compared to OpenAI and Claude models
Weaknesses
Queries are not within scope. May not have the same level of support as the more established models. Performance varies based on task complexity.
Use Cases
For research purposes, where the chatbot does not have to adhere to a corporate brand but is allowed freedom to access the web.
Description
AI model designed by Google with real time information access.
Costs
5 messages per user query and chat response
Strengths
Longer context window, improved coding abilities, intelligence on par with GPT-4 and Claude 3.5.
Weaknesses
Can be overly verbose at times.
Use Cases
Ideal for questions requiring detailed analysis and for tasks involving complex problem-solving.
Description
Fastest and cheapest model for many simple tasks.
Costs
1 messages per user query and chat response
Strengths
Fastest and cheapest model for many simple tasks. Suitable alternative to the GPT-4mini and Claude Haiku models. EU based AI model.
Weaknesses
Limited to simple queries. May not have the same level of support as the more established models. Performance varies based on task complexity.
Use Cases
FAQ bots trained on a simple knowledge base that provide quick answers to common questions without needing in-depth analysis or a nuanced understanding of the user’s query.
Description
A model optimized for fast responses, designed for real-time applications.
Costs
5 messages per user query and chat response
Strengths
Fast response times suitable for immediate interactions.
Weaknesses
May sacrifice depth and detail in responses for speed.
Use Cases
Chatbots that require quick, straightforward answers to user inquiries.
Description
An open-source model that is lightweight and customizable
Costs
1 message per user query and chat response
Strengths
A model with a large context window, allowing it to have decent quality responses at a relatively low cost
Weaknesses
It may not be able to follow instructions from the base prompt as accurately as Claude and OpenAI models. It may also perform more poorly in multilingual tasks.
Use Cases
Chatbots that require quick, straightforward answers to user inquiries.
Description
The GPT router helps to route your queries to the best OpenAI model available to answer your user query
Costs
5 message per user query and chat response
Strengths
The smart router picks the best model available for your user query, allowing you to potentially save on the cost of using a more expensive model.
Weaknesses
Chat response quality may be inconsistent, as the router may not always route to the smartest model.
Use Cases
Chatbots that do not strictly need to stick to their corporate brand or base prompts, used as a simple virtual assistant such as a chatbot embedded within a. small scale help center.