Both GPT-4.0 and GPT-4.0 Turbo are powerful language models developed by OpenAI. While they share some similarities, there are a few key differences to consider:

Input and Output:

• Both models can process text and image inputs, but only generate text outputs. GPT-4.0 Turbo additionally supports voice input and output, which GPT-4.0 will be adding in the coming weeks.

Context Window:

• Both models have a context window of 128,000 tokens, allowing them to maintain and utilize a large amount of contextual information.

Cost:

• The cost of using GPT-4.0 is half that of GPT-4.0 Turbo, making it a more cost-effective option for certain use cases.

Performance:

• In terms of generation speed, GPT-4.0 appears to significantly outperform GPT-4.0 Turbo, with latency metrics showing nearly a 50% reduction.
• The responses generated by GPT-4.0 also tend to be more detailed and informative compared to GPT-4.0 Turbo.

Overall, the choice between GPT-4.0 and GPT-4.0 Turbo will depend on the specific requirements of your use case, such as the need for voice capabilities, budget constraints, and the desired level of performance and detail in the generated outputs.

In this section, we will dive into the capabilities of GPT 4.0 by exploring the OpenAI Playground. We will test the model's abilities in image understanding and text generation, and compare its performance with GPT 4.0 Turbo.

First, we will select the GPT 4.0 model from the list of available models in the OpenAI Playground. We will set the system prompt to "You are a helpful assistant" and adjust the temperature and max tokens to our preferences.

Next, we will upload an image and ask GPT 4.0 to explain it. The model will quickly process the image and provide a detailed response, identifying the key elements and their characteristics. We will then compare the speed of generation between GPT 4.0 and GPT 4.0 Turbo, showcasing the impressive performance of the GPT 4.0 model.

Moving on, we will explore the use of the GPT 4.0 API within a Python notebook. We will install and upgrade the necessary OpenAI packages, import the required libraries, and set up the API client. We will then test the model's capabilities by asking it to solve a simple math problem, provide information about itself, and generate a weekly workout routine in JSON format.

Furthermore, we will demonstrate the model's image understanding abilities by processing images both through uploaded files and image URLs. The model will accurately describe the contents of the images, including the details of a bar chart and the emotions expressed in a person's facial expression.

Finally, we will explore the function calling capabilities of GPT 4.0. We will create a mock data set for NBA game scores and define a function to retrieve the scores based on the team name mentioned in the user prompt. The model will successfully call the external function and provide the requested information.

Throughout this section, we will highlight the impressive performance and versatility of the GPT 4.0 model, showcasing its ability to handle a wide range of tasks, from text generation to image understanding and function calling.

In this section, we will explore how to integrate GPT 4.0 into your Python projects. We will cover the following topics:

1. Chatbots: We will create a simple chatbot using the GPT 4.0 model, demonstrating its text generation capabilities.

2. JSON Responses: We will learn how to use the GPT 4.0 model to generate JSON-formatted responses, which can be useful for building APIs and integrating with other systems.

3. Function Calling: We will explore the function calling abilities of GPT 4.0, allowing the model to execute external functions and incorporate their results into the final response.

Throughout this section, we will provide concise and to-the-point explanations, focusing on the practical implementation details. Let's dive in!

In this tutorial, we have explored the capabilities of GPT-4.0, the latest language model from OpenAI. We have compared it to the GPT-4.0 Turbo model, highlighting the differences in input/output capabilities, context window, and cost.

We then delved into the OpenAI Playground, where we experimented with image processing, text generation, and function calling. The results showcased the impressive speed and accuracy of GPT-4.0, outperforming its predecessor, GPT-4.0 Turbo.

Next, we transitioned to using the GPT-4.0 API within a Python notebook, demonstrating how to install the necessary packages, authenticate with the API, and leverage the model's capabilities for tasks such as math problem-solving, question-answering, and JSON-formatted output generation.

Finally, we explored the model's function calling abilities, where we created a custom tool to retrieve NBA game scores based on user input. This highlighted the model's ability to integrate external tools and data sources to provide comprehensive and tailored responses.

While we did not cover voice input/output and video processing in this tutorial, the presenter mentioned the possibility of creating a separate video on those topics if there is interest from the audience.

Overall, this tutorial provided a comprehensive introduction to GPT-4.0 and its various use cases, equipping you with the knowledge and tools to get started with this powerful language model in your own projects.