ChatGPT, developed by OpenAI, is a powerful language model based on the GPT (Generative Pre-trained Transformer) architecture. The GPT series, which includes versions like GPT-2, GPT-3, and the more advanced GPT-4, revolutionized natural language processing (NLP) by enabling machines to generate human-like text. These models are used in various applications, from chatbots to content creation, coding assistance, and more.

The core functionality of ChatGPT revolves around predicting the next word in a sentence based on the context provided. This ability, while seemingly simple, enables the model to engage in complex conversations, answer questions, and even understand the nuances of human language. Therefore, when you connect it to your direct messages through our application, it can work as a 24/7 sales assistant for your business.

As ChatGPT has evolved, OpenAI has released several models, or versions, of it. Each model has unique capabilities and trade-offs. Therefore, when integrating ChatGPT into your application, it’s essential to choose the right model to suit your specific requirements. In this article, we’ll examine the main characteristics of the most popular models  and provide guidance on selecting the best model for your chatbot.

Clarifying Key Terms: What Are Tokens?

To understand how ChatGPT models work and how costs are calculated, let’s define tokens and how they impact chatbot performance:

Tokens: Tokens are chunks of text. They can be as short as one character or as long as one word. For instance:

• The word “chatbot” counts as one token.
• A sentence like “How are you?” is roughly 4 tokens.

Tokens include both the text you send to the model (input) and the text the model generates as a response (output).

Token Usage in Chatbots: Every interaction with a chatbot involves two stages:

• Interpreting the Input: When a user sends a message, the model uses tokens to process and understand the input based on its training data.
• Generating the Output: The model then generates a response, which also uses tokens. The length and complexity of the response determine the number of output tokens.

For example, if you send a message with 30 tokens and the model responds with 50 tokens, the total token usage for that interaction is 80 tokens.

How Tokens Affect Costs and Model Choice

The number of tokens used per interaction directly influences the cost of using a ChatGPT model. Different models have varying costs per token for input and output. When planning your chatbot, consider both the token limits (the maximum input/output the model can handle in a single interaction) and the costs per token to balance quality and budget. In this task, OpenAI’s Tokenizer can be a valuable help.

Where do you change the model?

You can select only one GPT model for all the accounts connected to our app. To change the model you are using on your AI chatbots, follow the steps below.

Open to your account’s OpenAI Settings. Open the app. On the left sidebar, scroll down and tap on the Settings icon. Once inside, go to the OpenAI section.

Select the model. You will find the ones available on the Prompt Model field. Do not forget to save.

Overview of ChatGPT Models

GPT-3.5 Turbo

• Language Interpretation: Strong language understanding and response capabilities for general purposes.
• Max Input Prompt: 4,096 tokens.
• Cost: Low cost per token, making it ideal for budget-conscious applications.
• Training Conversations: Built on a vast dataset up to 2023, covering diverse domains.
• Proclivity to Invent: More prone to “hallucinating” (inventing information) than GPT-4 models but sticks reasonably well to prompts.

Conclusion: Best for budget-friendly chatbots, general-purpose FAQs, and low-complexity conversations.

GPT-3.5 Turbo 16k

• Language Interpretation: Similar to GPT-3.5 Turbo but can handle longer, more complex prompts and contexts.
• Max Input Prompt: 16,384 tokens.
• Cost: Slightly higher than GPT-3.5 Turbo.
• Training Conversations: Extended context window helps manage detailed conversations.
• Proclivity to Invent: Similar to GPT-3.5 Turbo but performs better in maintaining coherence over longer exchanges.

Conclusion: Best for use cases requiring extended context, such as document summarization or detailed support.

GPT-4

• Language Interpretation: Advanced natural language understanding with nuanced and accurate responses.
  Max Input Prompt: 8,192 tokens.
• Cost: Higher per-token cost compared to GPT-3.5 models.
• Training Conversations: Trained on a broader and more diverse dataset, enhancing precision in complex scenarios.
• Proclivity to Invent: Significantly less prone to hallucinating compared to GPT-3.5 models; highly reliable for accurate and contextual responses.

Conclusion: Best for high-quality chatbots for industries like healthcare, finance, or legal assistance where accuracy is paramount.

GPT-4 32k

• Language Interpretation: Retains GPT-4’s accuracy with the ability to handle significantly longer inputs.
• Max Input Prompt: 32,768 tokens.
• Cost: Premium pricing due to extended token capacity.
• Training Conversations: Ideal for complex workflows with massive context windows.
• Proclivity to Invent: Similar to GPT-4; highly accurate with minimal hallucinations.

Conclusion: Best for analyzing lengthy documents, multi-step reasoning, and intricate workflows.

GPT-4 Turbo

• Language Interpretation: Comparable to GPT-4 but optimized for cost-effectiveness.
• Max Input Prompt: Matches GPT-4 at 8,192 tokens.
• Cost: Cheaper than GPT-4, making it an attractive option for businesses needing high-quality interactions at scale.
• Training Conversations: Designed for similar use cases as GPT-4 but with reduced latency and cost.
• Proclivity to Invent: Performs on par with GPT-4 in sticking to the prompt.

Conclusion: Best for high-quality chatbots that need scalable and cost-effective solutions.

GPT-4 0125

• Language Interpretation: Advanced, with tweaks to prioritize speed and stability.
• Max Input Prompt: 8,192 tokens.
• Cost: Slightly lower than standard GPT-4.
• Training Conversations: Enhanced focus on practical application with large data inputs.
• Proclivity to Invent: Balances accuracy with faster response times, with slight trade-offs in nuanced interpretation.

Conclusion: Best for fast-paced environments where speed and cost optimization matter, such as e-commerce chatbots.

GPT-4o

• Language Interpretation: Optimized for efficient performance with strong general language skills.
• Max Input Prompt: 8,192 tokens.
• Cost: Mid-range pricing.
• Training Conversations: Designed for real-world chatbot applications, balancing precision and speed.
• Proclivity to Invent: Highly reliable for sticking to the prompt and minimizing hallucinations.

Conclusion: Best for enterprise-level chatbots, customer support, and dynamic interaction use cases.

GPT-4o Mini

• Language Interpretation: Lightweight, cost-effective, and remarkably accurate for most chatbot needs.
• Max Input Prompt: 8,192 tokens.
• Cost: Lowest cost among GPT-4 models.
• Training Conversations: Focused on maintaining high-quality output while minimizing computational overhead.
• Proclivity to Invent: Excellent at adhering to prompts with minimal hallucination.

Conclusion: Best for general-purpose chatbots that balance cost and quality, such as startups or businesses scaling customer interactions.

Recommendations Based on Use Cases

In conclusion, these are the best models for each kind of business:

• General-Purpose Chatbots: GPT-4o Mini strikes the perfect balance between cost and performance, making it ideal for most scenarios.
• Complex Industries: Use GPT-4 or GPT-4 Turbo for their accuracy and reliability in handling nuanced information.
• Budget Constraints: GPT-3.5 Turbo offers affordability without compromising too much on quality.
• Long Conversations: Choose GPT-3.5 Turbo 16k or GPT-4 32k for extended context needs.

By carefully assessing your chatbot’s requirements, you can select the best model to deliver a superior user experience while optimizing costs. If you need extra guidance, our experts can help you here.