What Is GPT-4 and Why Is It More Powerful Than GPT-3?

May 16, 2023

GPT-4, the newest version of ChatGPT, OpenAI’s language model, is a breakthrough in artificial intelligence (AI) technology that has transformed how we communicate with machines. ChatGPT’s multimodal capabilities enable it to process text, images, and videos, making it an incredibly versatile tool for marketers, businesses, and individuals alike.

While ChatGPT is an application, GPT is the brain behind it. Let’s take a look at the evolution of this AI technology. 

What Is GPT?

Generative Pre-Trained Transformer is a neural network-based language model developed by Open AI that takes inputs to produce human-like text. Built on Transformer architecture, it analyzes Natural Language queries and predicts the best response based on its understanding of language. To be able to do that, GPT models depend on the knowledge that they gain after they are trained with billions of parameters of massive language datasets. 

GPT models are based on pre-trained Deep Learning algorithms with massive amounts of text data, which allows them to learn the structure and patterns of language. A user “feeds” the model with a prompt or sentence, and the transformer writes coherent information gathered from publicly available datasets.

These models take the input’s context into consideration, which is why they can attend to different parts of the input, making them capable of generating longer responses instead of just the next word in a sequence.


Writing text that is understandable to humans has been a difficult task for machines given that they don't know the complexities and nuances of the human language. GPT-3, however, is trained to generate realistic human text.

GPT3, released in May 2020 by OpenAI, offers several features that make it stand out from other Natural Language Processing (NLP) models. It can generate content based on a given context and based on a specific topic. This makes it extremely useful for dialogue generation, text completion, question-answers, and summarisation. Moreover, GPT-3 is capable of creating text in a variety of languages, allowing developers to create applications that can generate text in any language. Beyond human language text, it can also generate programming codes. 


GPT-4 is the latest and most advanced model launched by Open AI in March 2023. It is the first multimodal model of its kind, which implies that it accepts inputs in the form of text as well as images. It is way smarter than GPT-3 since it is ten times more creative. The official product update mentions that “it can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.”

GPT-4 also has considerable reasoning capabilities. Let’s say you want to schedule a meeting keeping in mind the availability of multiple people – you can ask GPT-4 to find times that work for everyone involved. While it may be a simple task, it clearly exemplifies the logical reasoning capabilities of GPT-4.

GPT-3 vs GPT-4

While the data input for GPT-3 and GPT-4 is the same, GPT-4 is more creative and more reliable. Some of the new features are:

  • It can process up to 25,000 words in input and output combined, which is eight times the limit of GPT-3. In addition, the context window has also increased. GPT-3 has 4,096 and 2,049 tokens, while GPT-4 has 8,129 and 32,768 tokens.
  • Improvements in reasoning and understanding. Texts are understood better and better reasoning is performed on them.
  • Content generated on GPT-4 is less prone to be flagged as machine-generated because they are more human-like and use certain sentence features to make it feel more personal.

Some of the things that have considerably improved in GPT-4 are:

  • Better Steerability
    GPT-4 is more steerable in allowing users to specify the tone, style, or voice in which the text needs to be written. This steerability has improved considerably from GPT-3, which could generate different types of content but required a lot of retraining. 
  • Text and Image Input
    GPT-4 is a multi-modal model, which means that it accepts text inputs as well as images. It recognizes and understands an image’s contents and can make logical analysis from the image with human-like accuracy. Since GPT-4 can also use image inputs, it enables users to specify any vision or language task by entering both text and images. 
  • Human-Level Performance of Benchmarks
    GPT-4 achieved human-level performance on several professional and academic benchmarks, including the Uniform Bar Examination, LSAT, and SAT. It also outperformed other large language models on traditional Machine Learning (ML) benchmarks, as well as in multiple-choice questions in 57 subjects, commonsense reasoning, and grade-school science questions.

To summarize - GPT-4 scores 40% higher than the previous version GPT-3.5 on factuality evaluations. Ranging from history, math, code and science, it outperforms older models and is poised to see quicker adoption across industries. 

Leveraging LLMs in Enterprises

Despite being extremely powerful models, GPT-3 and GPT-4 are closed-source and available only as a “model-as-a-service” via a paid API. Most organizations don’t have the resources to develop such models themselves, which is why it is easier to use open source models to develop their own applications. Once these models are adapted on internal data, companies can achieve higher business value.

One advantage of open source LLMs is that they can be more customizable and configurable than GPT, as their source code is available for modification and adaptation. This can be particularly important in enterprise settings, where data and requirements may vary widely from one organization to another. Additionally, open source LLMs can offer greater transparency and control over the data processing pipeline, which can be important for organizations that need to comply with regulatory requirements or protect sensitive data.

Businesses can deploy their open-source LLMs on private infrastructure, as well as on public cloud. 

LLMs can be deployed on private infrastructure keeping in mind a range of factors, including hardware requirements, infrastructure setup, and software installation. Deploying LLMs on a private cloud will require management of a data center, which can be challenging. However, it also provides greater control and customization over your infrastructure, which can be beneficial for organizations with specific security or compliance requirements.

Alternatively, you can deploy your model on Public Cloud. Deploying Large Language Models (LLMs) on a public cloud is a popular option due to the flexibility and scalability it offers. Public cloud providers such as E2E Networks offer various tools and services to make LLM deployment and management easier. To deploy an LLM on a public cloud, you will need to consider factors such as hardware requirements, data transfer, and software installation. Public cloud providers offer a range of virtual machines with different specifications to meet the hardware requirements of the LLM. Additionally, public cloud providers offer tools such as data transfer services, object storage, and network configuration options to make data processing and transfer more efficient. Once the infrastructure is set up, you can install the necessary software such as TensorFlow or PyTorch and train and test the LLM. Public cloud providers offer services for managing and scaling the infrastructure as per the LLM's requirements. 

E2E Networks is a public cloud service provider which can help you deploy your LLMs using our GPUs. To know more, you can get in touch with sales@e2enetworks.com




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The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

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  • It can also help in completing DNA sequences.

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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What is Q-Learning Algorithm?

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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

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What does GAUDI do?

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  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
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  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
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