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Exploring the Difference Between GPT-4 and ChatGPT

6min Read

Unlock the mysteries of AI as we dissect the differences between GPT-4 and ChatGPT. Delve into their features, strengths, and the best ways to use these revolutionary tools.

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Artificial intelligence (AI) has come a long way in recent years, with language models like Generative Pre-trained Transformer 4(GPT-4) and ChatGPT leading the charge in transforming how we communicate and access information. 

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In this article, we will delve into the key differences between these two groundbreaking AI models, exploring their training methods, capabilities, limitations, and more. 

Introduction to ChatGPT

Visual representation of ChatGPT – Image via Pixabay

ChatGPT is a conversational AI chatbot developed by OpenAI. Built on the GPT-3.5 model, it generates detailed text responses based on input prompts. ChatGPT feeds on billions of sentences, enabling it to understand the context and generate engaging content. 

A key differentiator for ChatGPT is its use of reinforcement learning from human feedback (RLHF), an innovative approach that allows the model to learn from human-generated data and refine its responses over time. 

This makes ChatGPT an increasingly popular tool for businesses seeking to enhance their customer service, marketing, content creation, and communication strategies.

Introduction to GPT-4

GPT-4 is the latest and most advanced language model from OpenAI. Released on March 14, 2023, it builds upon the successes of its predecessors, including ChatGPT, and offers significant improvements in scaling up deep learning (DL) and generative AI capabilities. 

Unlike GPT-3.5, GPT-4 is multimodal, meaning it can accept both text and image inputs. This allows for a more comprehensive understanding of context and the generation of more nuanced responses. 

GPT-4 is a transformer model pre-trained to predict the next token in a sequence and fine-tuned using RLHF, leveraging both public and licensed third-party data.

Comparing GPT-4 and ChatGPT

Training methods: RLHF

A core element that separates GPT-4 and ChatGPT from traditional language models is their use of RLHF in the training process. While traditional language models feed on large corpora of text to predict the next word in a sentence or the most likely sequence of words given a prompt, RLHF involves training the language model using feedback from human evaluators. 

This serves as a reward signal responsible for evaluating the quality of the produced text, allowing the model to generate text, more likely to be rated highly by humans.

Both GPT-4 and ChatGPT employ RLHF during their training, using human annotators to rank multiple generated responses based on quality. 

These preferences are then converted into a scalar value used to train a reward model, which in turn fine-tunes the policy model (the language model that generates text) using reinforcement learning algorithms like proximal policy optimization (PPO).

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Model architecture: Transformer-based

GPT-4 and ChatGPT are both built on transformer architecture, a deep learning (DL) framework that has revolutionized natural language processing (NLP). Transformer models, such as GPT-2 and GPT-3, use multi-headed self-attention mechanisms to decide which text inputs to prioritize.

They also employ decoder-only architecture to generate output sequences one token at a time, iteratively predicting the next token in a sequence. While the exact architectures for GPT-4 and ChatGPT have not been released, they are likely to follow the same decoder-only structure as their predecessors.

Comparison between GPT-4 and ChatGPT – Image via Pixabay

Capabilities: ChatGPT vs. GPT-4

Text generation and summarization

Both ChatGPT and GPT-4 excel in generating coherent, contextually relevant, and engaging responses in a conversational manner. They are capable of summarizing information, allowing users to quickly grasp the main points of lengthy articles or documents. 

Multimodal input and image understanding

A key advantage of GPT-4 over ChatGPT is its ability to process multimodal inputs, including images. GPT-4 can analyze images and generate captions, classifications, and analyses based on the visual information provided. 

This functionality is not yet available in ChatGPT, which only accepts text inputs.

Creative and technical writing

GPT-4 receives appreciation for its creative abilities, with the official product update highlighting its potential in generating, editing, and collaborating on creative and technical writing tasks, such as composing melodies, crafting screenplays, or even mastering a user’s writing style.

Performance: ChatGPT vs GPT-4 

In terms of performance, GPT-4 shows significant improvement over its predecessor GPT-3.5 (the model behind ChatGPT). OpenAI claims that GPT-4 is more accurate, less likely to produce harmful or inappropriate content, and better at handling sensitive topics. 

However, it is unclear whether these enhancements are due to the GPT-4 model itself or the additional adversarial testing employed during its training process. This model also outperforms GPT-3.5 on various academic and professional exams, scoring in the 90th percentile on the Uniform Bar Exam compared to GPT-3.5’s 10th percentile score. 

GPT-4 demonstrates superior performance on traditional language model benchmarks and other state-of-the-art models as well.

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GPT-4 vs ChatGPT: Limitations and risks

Outdated knowledge and short-term memory

Both GPT-4 and ChatGPT are limited by their training data, which becomes outdated over time. ChatGPT is built on GPT-3.5, which is trained on data up to 2021, meaning it lacks knowledge of more recent events or trends. 

GPT-4, while more up-to-date, still faces similar limitations as its data only extends up to August 2022. Both models also suffer from short-term memory issues, which can affect their ability to maintain context in longer conversations.

Inaccurate information and hallucinations

Despite their impressive language understanding capabilities, GPT-4 and ChatGPT may occasionally generate erroneous or misleading information, a phenomenon known as hallucinations. 

While GPT-4 reportedly experiences this issue less frequently than ChatGPT, it is not immune to the problem.

Applications in real-world scenarios 

GPT-4 and ChatGPT have numerous practical applications across various industries, including customer service, marketing, content creation, and communication. 

Their ability to generate coherent, contextually relevant, and engaging text makes them valuable tools for businesses looking to enhance their digital presence and streamline their interactions with customers and clients.

Pricing and availability

GPT-4 is currently available exclusively to ChatGPT Plus subscribers, while ChatGPT is majorly accessed through OpenAI’s ChatGPT website. 

Additionally, users looking to experience GPT-4’s advanced capabilities will need to join the ChatGPT Plus waitlist and subscribe to the services. 

Users can benefit from both ChatGPT and GPT-4 – Image via Pixabay

Wrapping up

In this comprehensive comparison of GPT-4 and ChatGPT, we have explored their key differences and similarities in terms of training methods, capabilities, limitations, and applications. Understanding the evolution of these AI language models helps us appreciate the potential future of smart communication and information access. 

With their ability to generate high-quality text and respond to a wide range of prompts, GPT-4 and ChatGPT are undoubtedly revolutionizing the world of AI-assisted language generation.


Prakriti is a Content Writer at AMBCrypto. She describes herself as a passionately creative individual, with a dash of strategic prowess. With over 3.5 years of experience in the field of content writing and marketing, she is dedicated to churning out top-notch content in domains like Crypto, Web 3.0, AI and contributing to quench the thirst for technical knowledge of her readers.
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