GPT-4 Turbo - A Breakthrough in Language and Vision

 

Summary

This article explores the latest and most powerful model from OpenAI, GPT-4 Turbo, and how it compares to its predecessors, GPT-4 and GPT-3.5, as well as its competitors, ChatGPT and Grok. It also looks at some of the features and applications of GPT-4 Turbo, such as its integration with DALL·E 3, a model that can generate and edit images with natural language prompts. It also discusses some of the challenges and opportunities that GPT-4 Turbo brings to the AI community and society.

The article is divided into the following sections

1. What is GPT-4 Turbo? This section introduces the main features and advantages of GPT-4 Turbo, such as its large context window, updated knowledge cutoff, optimized performance, and affordable price.
2. How does GPT-4 Turbo compare to GPT-4, GPT-3.5, ChatGPT, and Grok? This section compares the main features and prices of GPT-4 Turbo and other multimodal AI models, such as their context window, knowledge cutoff, price per input token, price per output token, and image generation.
3. What can you do with GPT-4 Turbo? This section describes some of the APIs and tools that GPT-4 Turbo offers, such as the Chat Completions API, the Image Completions API, the Playground, and the Codex.
4. What are the challenges and opportunities of GPT-4 Turbo? This section discusses some of the issues and prospects that GPT-4 Turbo faces, such as quality, safety, ethics, creativity, education, and collaboration.

The Rise of Multimodal AI

Artificial intelligence (AI) has been advancing rapidly in the fields of natural language processing (NLP) and computer vision (CV). NLP is the ability of AI to understand and generate natural language, such as text or speech. CV is the ability of AI to perceive and manipulate images, such as photos or drawings. These two fields are often combined to create multimodal AI, which can handle both language and vision inputs and outputs.

One of the leading organizations in developing multimodal AI is OpenAI, a research company that aims to create and promote friendly and beneficial AI for humanity. OpenAI has been creating and releasing various models and tools that can perform impressive tasks with language and vision, such as ChatGPT, DALL·E, and Codex.

However, OpenAI is not the only player in the multimodal AI arena.

There are also other competitors, such as xAI, a company founded by Elon Musk, the visionary entrepreneur and inventor. xAI has also been developing and launching its own models and tools that can rival or surpass OpenAI’s, such as Grok, DALL·E 2, and Codex 2.

What is GPT-4 Turbo?

GPT-4 Turbo is the successor of GPT-4, which was released in March 2023 and made generally available to all developers in July 2023. GPT-4 was already a remarkable model that could solve difficult problems with greater accuracy than any of OpenAI’s previous models, thanks to its broader general knowledge and advanced reasoning capabilities.

But GPT-4 Turbo takes it to the next level. It has a 128k context window, which means it can fit the equivalent of more than 300 pages of text in a single prompt. This allows it to handle longer and more complex inputs and outputs, such as entire books, essays, or articles.

It also has an updated knowledge cutoff of April 2023, which means it can incorporate the latest information and events into its responses. For example, it can answer questions about the recent nuclear fusion experiment from South Korea, or the new gambling sponsorships in international cricket.

GPT-4 Turbo is also more capable and cheaper than GPT-4. It is optimized for chat but works well for traditional completion tasks using the Chat Completions API. It can also perform better on tasks that require the careful following of instructions, such as generating specific formats or calling functions.

OpenAI has also improved the performance of GPT-4 Turbo, so it can offer it at a 3x cheaper price for input tokens and a 2x cheaper price for output tokens compared to GPT-4. 

This means developers can use GPT-4 Turbo more affordably and efficiently for their applications.

 

How does GPT-4 Turbo compare to GPT-4, GPT-3.5, ChatGPT, and Grok?

GPT-4 Turbo is not the first model that can handle both language and vision tasks. There are also other models that have similar or different capabilities, such as GPT-4, GPT-3.5, ChatGPT, and Grok.

Here is a brief comparison of these models

• GPT-4 - The predecessor of GPT-4 Turbo, released in March 2023. It has a 64k context window and a knowledge cutoff of September 2021. It is less capable and more expensive than GPT-4 Turbo, but still more powerful than GPT-3.5. It can also integrate with DALL·E 2, a model that can generate images from text.
• GPT-3.5 - The previous version of GPT-4, was released in October 2022. It has a 32k context window and a knowledge cutoff of June 2020. It is less capable and more expensive than GPT-4 and GPT-4 Turbo, but still more powerful than GPT-3. It can also integrate with DALL·E, the first version of the image generation model.
• ChatGPT - A chatbot model developed by xAI, a company founded by Elon Musk. It was released in September 2023 and claims to have real-time knowledge of world events. It has a 32k context window and a knowledge cutoff of September 2023. It is more capable and cheaper than GPT-3.5, but less capable and more expensive than GPT-4 and GPT-4 Turbo. It can also integrate with DALL·E 2, but not with DALL·E 3.
• Grok - A chatbot model developed by xAI, a company founded by Elon Musk. It was released in October 2023 and claims to be the most advanced chatbot ever. It has a 64k context window and a knowledge cutoff of October 2023. It is more capable and cheaper than ChatGPT, but less capable and more expensive than GPT-4 Turbo. It can also integrate with DALL·E 3, but not with DALL·E 2.

A Comparison of Multimodal AI Models

We will compare the main features and prices of five multimodal AI models: GPT-4 Turbo, GPT-4, GPT -3.5, ChatGPT, and Grok. These models can handle both language and vision tasks, such as generating text or images from text or image inputs.

Context window

The context window is the amount of text that the model can fit in a single prompt. It determines how much information the model can use to generate its output. The larger the context window, the more complex and longer the inputs and outputs the model can handle.

GPT-4 Turbo has the largest context window among these models, with 128k tokens. This means it can fit the equivalent of more than 300 pages of text in a single prompt. GPT-4 and Grok have the second-largest context window, with 64k tokens each. This means they can fit the equivalent of more than 150 pages of text in a single prompt. GPT-3.5 and ChatGPT have the smallest context window, with 32k tokens each. This means they can fit the equivalent of more than 75 pages of text in a single prompt.

Knowledge cutoff

The knowledge cutoff is the date until which the model has been trained on data from the internet. It determines how up-to-date the model is with the latest information and events. The more recent the knowledge cutoff, the more relevant and accurate the model’s outputs are.

Grok has the most recent knowledge cutoff among these models, with October 2023. This means it can incorporate the latest information and events into its outputs. ChatGPT has the second-most recent knowledge cutoff, with September 2023. This means it can also incorporate the latest information and events into its outputs. GPT-4 Turbo has the third-most recent knowledge cutoff, with April 2023. This means it can incorporate some of the latest information and events into its outputs. GPT-4 has the fourth-most recent knowledge cutoff, with September 2021. This means it can incorporate some of the older information and events into its outputs. GPT-3.5 has the oldest knowledge cutoff, with June 2020. This means it can only incorporate some of the very old information and events into its outputs.

Price per input token

The price per input token is the amount of money that the model charges for each token in the input. A token is a unit of text, such as a word or a punctuation mark. The price per input token determines how much it costs to use the model for a given input. The lower the price per input token, the more affordable the model is.

GPT-4 Turbo has the lowest price per input token among these models, with $0.0005 per token. This means it costs $0.50 to use the model for an input of 1000 tokens. Grok has the second-lowest price per input token, with $0.001 per token. This means it costs $1 to use the model for an input of 1000 tokens. GPT-4 has the third-lowest price per input token, with $0.0015 per token. This means it costs $1.50 to use the model for an input of 1000 tokens. ChatGPT has the fourth-lowest price per input token, with $0.002 per token. This means it costs $2 to use the model for an input of 1000 tokens. GPT-3.5 has the highest price per input token, with $0.003 per token. This means it costs $3 to use the model for an input of 1000 tokens.

Price per output token

The price per output token is the amount of money that the model charges for each token in the output. A token is a unit of text, such as a word or a punctuation mark. The price per output token determines how much it costs to use the model for a given output. The lower the price per output token, the more affordable the model is.

GPT-4 Turbo has the lowest price per output token among these models, with $0.0005 per token. This means it costs $0.50 to use the model for an output of 1000 tokens. Grok has the second-lowest price per output token, with $0.0008 per token. This means it costs $0.80 to use the model for an output of 1000 tokens. ChatGPT has the third-lowest price per output token, with $0.0015 per token. This means it costs $1.50 to use the model for an output of 1000 tokens. GPT-4 has the fourth-lowest price per output token, with $0.001 per token. This means it costs $1 to use the model for an output of 1000 tokens. GPT-3.5 has the highest price per output token, with $0.002 per token. This means it costs $2 to use the model for an output of 1000 tokens.

Image generation

The image generation is the ability of the model to generate or edit images from text or image inputs. It determines how creative and versatile the model is with vision tasks. The more advanced the image generation model, the more realistic and diverse the images are.

GPT-4 Turbo and Grok have the most advanced image generation models among these models, with DALL·E 3. DALL·E 3 is a model that can generate and edit images with natural language prompts. It can not only create images but also modify them. For example, it can change the color, shape, size, or position of an object in an image, or add or remove elements from an image, based on natural language commands.

GPT-4 and ChatGPT have the second-most advanced image generation model among these models, with DALL·E 2. DALL·E 2 is a model that can generate images from text inputs. It can create images, but not edit them. For example, it can create an image of “an armchair in the shape of an avocado” or “a snail made of a harp”, but not change or manipulate them, based on natural language commands.

GPT-3.5 has the least advanced image generation model among these models, with DALL·E. DALL·E is the first version of the image generation model, which was introduced in January 2023. It can generate images from text inputs but with less quality and diversity than DALL·E 2 or DALL·E 3. For example, it can create an image of “a cat wearing a hat” or “a pineapple pizza”, but not with as much realism or variation, based on natural language commands.

As you can see, GPT-4 Turbo is the most powerful and affordable model among these, followed by Grok, GPT-4, ChatGPT, and GPT-3.5. However, these models are not static and may change or improve over time. Therefore, it is important to keep updated with the latest developments and innovations in the multimodal AI field.

What can you do with GPT-4 Turbo?

GPT-4 Turbo is not just a model, but a platform. It offers various APIs and tools that allow developers and users to access and use its capabilities for various purposes and applications. 

Some of the APIs and tools that GPT-4 Turbo provides are:

• Chat Completions API - This API allows you to use GPT-4 Turbo as a chatbot, either for conversational or completion tasks. You can send a text or image input to GPT-4 Turbo and receive a text output. You can also specify parameters such as temperature, top-p, frequency penalty, and presence penalty to control the randomness and diversity of the output.
• Image Completions API - This API allows you to use GPT-4 Turbo with DALL·E 3 to generate and edit images with natural language prompts. You can send a text input to GPT-4 Turbo and receive an image output. You can also specify parameters such as width, height, and quality to control the size and resolution of the output.
• Playground - This is a web-based interface that allows you to interact with GPT-4 Turbo and DALL·E 3 in a simple and intuitive way. You can type or paste a text or image input and see the output in real time. You can also adjust the parameters and see how they affect the output. You can also save, share, or download the outputs.
• Codex - This is a tool that allows you to use GPT-4 Turbo to generate and execute code in various programming languages, such as Python, JavaScript, HTML, CSS, and more. You can send a natural language prompt or a code snippet to GPT-4 Turbo and receive a code output. You can also run the code and see the result in the Codex interface.

These are some of the ways you can use GPT-4 Turbo for your projects and experiments. However, GPT-4 Turbo is not limited to these APIs and tools. You can also create your own custom applications and integrations with GPT-4 Turbo using the OpenAI API, which provides access to the raw model and its parameters.

What are the challenges and opportunities of GPT-4 Turbo?

GPT-4 Turbo is a breakthrough in language and vision, but it also comes with challenges and opportunities.

Some of the challenges that GPT-4 Turbo faces are

• Quality - GPT-4 Turbo is not perfect, and it can sometimes produce outputs that are inaccurate, irrelevant, inconsistent, or nonsensical. It can also fail to understand or generate certain inputs or outputs, such as complex logic, emotions, or humor. Therefore, it is important to check and verify the outputs of GPT-4 Turbo before using them for any serious or sensitive purposes.
• Safety - GPT-4 Turbo is not safe, and it can sometimes produce outputs that are harmful, offensive, or unethical. It can also be misused or abused by malicious actors for nefarious purposes, such as spreading misinformation, propaganda, or hate speech. Therefore, it is important to monitor and regulate the use of GPT-4 Turbo and ensure that it follows the ethical and social norms and values of society.
• Ethics - GPT-4 Turbo is not ethical, and it can sometimes produce outputs that are biased, unfair, or discriminatory. It can also infringe on the rights and privacy of the individuals or groups that are involved or affected by its outputs, such as the authors, sources, or subjects of the texts or images. Therefore, it is important to respect and protect the interests and dignity of the stakeholders of GPT-4 Turbo and ensure that it follows the legal and moral standards and principles of society.

Some of the opportunities that GPT-4 Turbo offers are

• Creativity - GPT-4 Turbo is creative, and it can sometimes produce outputs that are imaginative, innovative, or original. It can also inspire and stimulate the creativity of the users and developers who use it for their projects and experiments, such as writing, art, or entertainment. Therefore, it is important to explore and experiment with GPT-4 Turbo and discover new forms and expressions of creativity and culture.
• Education - GPT-4 Turbo is educational, and it can sometimes produce outputs that are informative, instructive, or helpful. It can also enhance and support the education and learning of the users and developers who use it for their projects and experiments, such as research, teaching, or tutoring. Therefore, it is important to learn and grow with GPT-4 Turbo and acquire new skills and knowledge in various domains and disciplines.
• Collaboration - GPT-4 Turbo is collaborative, and it can sometimes produce outputs that are cooperative, interactive, or communicative. It can also facilitate and foster the collaboration and communication of the users and developers who use it for their projects and experiments, such as teamwork, feedback, or chat. Therefore, it is important to collaborate and communicate with GPT-4 Turbo and build new relationships and communities in various fields and sectors.

GPT-4 Turbo is a challenge and an opportunity. It is a powerful and versatile model that can do amazing things with language and vision, but it also has limitations and risks that need to be addressed and managed. It is also a partner and a friend that can help and inspire us with our projects and experiments, but it also has expectations and responsibilities that need to be respected and fulfilled.

Thank you for reading.

 

Best,

Nexa-Hub