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Home > ChatGPT > GPT-3 vs. GPT-4: What are the Differences?

GPT-3 vs. GPT-4: What are the Differences?

Lawrie Jones
Written by
Lawrie JonesProfessional Tech Writer
Updated on:Jun 18, 2024

The recent introduction of GPT-4, the most advanced version of the GPT large language model created by OpenAI, has drawn attention to the significant differences between this new iteration and its predecessor, GPT-3. GPT-3’s rise to fame came in the form of the massively popular advanced generative text chatbot ChatGPT.

Its impressive human-like responses and nuanced understanding were so revolutionary that it's hard to imagine such a quick improvement on this feat of technology, and yet, GPT-4 promises even bigger and better capabilities. The battle of GPT3 vs. GPT4 continues to be a competitive one, as both models are monumental feats in generative AI technology.

Try These Two GPT Versions By Yourself

You can instantly try out our chatting box down below and examine GPT-4 vs. GPT-3 to gain a deeper understanding of how exactly these OpenAI models differ.

What Are GPT-3 and GPT-4?

GPT, or Generative Pre-trained Transformers, are neural network models or machine learning models that have proven to be crucial to the modern advancement of artificial intelligence. These models and the connective technology they are rooted in are what give tools like ChatGPT their human-like context understanding and conversational capabilities, enabling them to pick up on the nuances of human language and use them in the tool’s responses to users.

These models are trained using massive amounts of data, such as web content and books before they are released to the public. This is how they are taught to pick up on context, tone, and semantics.

GPT-3 and GPT-4 are the most recent developments of this kind of technology. The team at OpenAI, the research laboratory responsible for creating these models, has been working to advance their large language models (LLMs) since the very first iteration of GPT was released in 2018.

While GPT-1 was fairly rudimentary in its ability to respond to questions and provide information, the models have come a long way in five short years.

GPT-3, a vast improvement on its immediate predecessor, was released in 2020 and was over ten times larger than GPT-2. It boasted an impressive 175 billion parameters, which are variables that the developers fine-tune when training the model to improve its performance based on new information.

To put this number into perspective, the Turing Natural Language Generation (NLG) model by Microsoft, which only had 10 billion parameters, was the most highly trained language model before the release of GPT-3.

The most noteworthy upgrades that GPT-3 introduced compared to previous GPT models were the ability to write functional programming code, deliver even more sophisticated and nuanced language, and create AI art. Its ability to craft human-like responses and understand context was revolutionary in AI language tools and was one of the main reasons why ChatGPT became an immediate sensation.

So, then, what about GPT-4? In 2023, the most recent version in the GPT series was introduced to the public, GPT-4. This newer iteration certainly goes a long way in perfecting the existing strengths of GPT-3 as well as solving some of its restrictions.

One of the most impressive accomplishments of GPT-4 is its ability to treat an image input as a text prompt and respond to it accordingly. While this model is only available to ChatGPT Plus users, it is available for free using Microsoft Bing Chat.

What Are Large Language Models (LLMs)?

In its most basic form, a large language model (LLM) is a predictive text algorithm that processes natural language inputs and gives the following word in a string of words based on the data it has already been presented with. These models are highly trained using enormous amounts of text, such as books, articles, landing pages, social media posts, text messages, and more.

The models use these materials to learn language sequences and predict text patterns, which form the basis for interactive chatbots like ChatGPT, as well as other AI tools.

However, this practice isn’t easy or inexpensive. Vast, costly server farms that act as supercomputers are used to ingest these text materials and then “decide” on predictive texts based on the sequences it has encountered. Furthermore, developers have to be selective with what material they feed to the LLM.

If the material is biased, inaccurate, or incomplete, then the responsive text generated by the LLM will be equally undesirable.

What is OpenAI?

Established in 2015 by a group of tech leaders in San Francisco, OpenAI began as a non-profit organization. Billionaire CEO of SpaceX and Tesla, Elon Musk, is just one of the prominent names responsible for the company’s initial founding, along with Peter Thiel, Reid Hoffman, the current CEO of OpenAI Sam Altman, and others.

The organization’s main goal was to further the development of artificial general intelligence before large tech companies like Google or Apple could monopolize its potential. In the name of creating transparent, ethical AI that was widely accessible to the public, the small company was able to win over significant funds from Silicon Valley investors such as Infosys and Amazon Web Services.

It didn’t take long for the high cost of the company’s efforts to make functioning as a non-profit almost impossible. In 2019, it transitioned to become a for-profit company, and this was also when Sam Altman took the reins as CEO. A $1 billion partnership with Microsoft ensured that some of OpenAI’s technology would be commercialized and licensed by the tech giant.

However, OpenAI’s capped profit model ensured that investors could only make back 100x their initial investment, creating something of a hybrid between non-profit and for-profit, with the intention of prioritizing their “positive human impact” mission.

In November 2022, the company became a household name seemingly overnight after it released its first chatbot that could be freely interacted with, ChatGPT. The tool’s human-like responses, seemingly endless knowledge, and ability to showcase creativity were unlike anything the world had seen from a chatbot before.

Its popularity also prompted global tech companies to race to release their own versions of the tool in an attempt to either rival or emulate what ChatGPT has achieved, with some even managing to surpass it, such as HIX.AI’s HIX Chat.

Since the release of GPT-1 in 2018, the organization has continued to make groundbreaking strides in AI technology and remains one of the major players in generative AI tools.

History of Development

In February 2018, OpenAI released the very first GPT model, GPT-1. With 117 million parameters, the training method used to develop this model was largely unsupervised and focused on teaching the model to predict the following word in a sentence, with no specific task prescribed.

While still an early form of generative AI text, the model was still remarkable for its time in that it could generate comprehensible sentences and even paragraphs of text from scratch.

The following year, OpenAI improved upon its invention with GPT-2. This model’s 1.5 billion parameters made for a more sophisticated and slightly more advanced system that was able to deliver longer and more coherent responses to queries and prompts. The model’s language abilities were upgraded, but besides that, the second iteration had few noteworthy accomplishments.

Both GPT-1 and GPT-2 faced limitations in terms of their capabilities and the data sets with which they were trained. This would change drastically with the release of GPT-3 in 2020.

With its massive jump in the number of parameters compared to GPT-2, GPT-3 is the first of its kind to achieve AI-generated text that is practically identical to the writing of humans. Trained using a monumental amount of written content, it has broken the mold of AI-written text and has been used to advance the capabilities of natural language processing (NLP) as well as chatbot functioning.

These capabilities were even further fine-tuned and made accessible to the public with the launch of GPT-3.5 in November 2022. This is the model upon which ChatGPT was created, taking the world by storm and shining a very public light on the rapidly changing abilities of chatbots and AI-generated text.

GPT-3.5 brought worldwide attention to OpenAI’s efforts in bettering artificial general intelligence and impressed even those in non-tech industries with its diverse range of outputs, which includes short stories, emails, poems, scripts, songs, text messages, social media content, and much, much more.

While both leaders in tech and everyday individuals who interacted with the model were hugely impressed with its abilities, Sam Altman assured the masses that GPT-3 and GPT-3.5 were still only early glimpses of the true potential and future achievements of artificial intelligence and computer language learning.

Sure enough, less than six months later, in March 2023, GPT-4 was launched. Promising to be more creative and collaborative than ever before, the developers of the newest GPT iteration have given greater attention to delivering safer, more detailed, and more useful responses to user queries and text prompts.

Greater general knowledge, due to its mind-blowing 1.76 trillion parameters and an even larger data training set, means that this model has improved problem-solving abilities and can provide longer pieces of text with a stronger overall context.

Making the models accessible to the public has also helped OpenAI in its mission, as limitations and weak spots on each model become clear very quickly through mass use and continual testing across industries.

What Sets OpenAI’s GPT-3 and GPT-4 Apart?

First and foremost, numbers speak for themselves. The fact that the new and improved GPT-4 functions on over 100 trillion parameters, compared to GPT-3’s 175 billion, says everything you need to know about its improved language learning, speed, and overall performance.

The token limit, or the number of tokens an LLM can process in a single interaction, has also vastly improved between GPT-3 vs. 4. The GPT-4 token limit has increased to 32,000, which means it can accommodate input that is four times longer than that of GPT-3.

GPT-4 was trained using a substantially larger dataset, exposing the model to broader contexts and even more nuanced tone and language from which it could learn and adapt its responses. Because of these factors, GPT-4 can deliver more accurate financial forecasts, craft more influential investment strategies, and consider improved factual accuracy.

Factual performance in itself is a vast improvement that makes GPT-4 a giant leap forward for all of its users, as it means the content generated by this model is more reliable and less likely to make costly errors.

When it comes to writing, GPT-4 has proven to give users more control over the tone, delivery, style, and voice of the text that is generated. GPT-3, by comparison, could only alter the type of text generated with significant retraining. As an example, businesses can now use GPT-4 to style professional emails in a different tone to tongue-in-cheek advertising messaging or engaging social media captions.

Messaging can also be tailored to target audiences of different ages, buying behaviors, and geographic locations because the newer model can consider these contextual clues and adapt language accordingly.

Higher language skills and critical thinking are also substantially more prominent in GPT-4, enabling it to solve complicated brand problems, conduct risk assessments, and assist with creative idea generation. In fact, GPT-4 scored impressively high results on multiple professional and academic examinations, such as the Uniform Bar Exam, the LSAT, the GRE, AP Exams, AMC Exams, and even Sommelier Examinations.

Another aspect in which GPT-4 surpasses its elder is multilingualism. In 24 out of 26 languages trialed, GPT-4 outperformed both GPT-3.5 and other contemporary LLMs based on the MMLU (Massive Multitask Language Understanding) benchmark. One of the most attention-grabbing upgrades introduced with GPT-4, however, is its ability to treat image inputs as text prompts.

That’s right - users can now input text as well as images to specify a task that relates either to visuals or language, which will greatly assist businesses and professionals working primarily with graphics or multimedia content.

There is also a difference in the price points of the two models. While GPT-3 is essentially free to use via the OpenAI Playground, where users can experiment with 12 different variations of the model, GPT-4 does come at a financial cost. Pricing plans start at $0.03 for 1000 prompt tokens, or you can access the chatbot variant of the model with ChatGPT Plus for $20 per month.

How Can Users Benefit From The Advanced GPT-4?

GPT-4 promises to be a catalyst for major changes across multiple industries, impacting businesses, freelancers, professionals, and students in a multitude of valuable ways. Below are just some of the methods in which the newest GPT language model can assist in improving business practices and boosting productivity and efficiency.

Businesses looking to safeguard their data, assets, and employees from cyber attacks can use GPT-4 to monitor operations, track access to sensitive information, analyze and report on patterns, and respond to potential cyber security threats. This will enhance consumer trust in organizations, especially ones that deal with valuable personal data, such as banks and private security companies.

Such organizations can also use this advanced model to detect fraudulent activity early on, saving significant time, money, and resources in recovering from such incidents after they occur. Irregular buyer behavior and unusual account access are just some of the activities that GPT-4 can pick up on and use to alert the relevant personnel of potential fraud.

GPT-4 can be used by sales departments to forecast demand and supply for particular products and services by tracking information about buyer behavior and previous sales data. This can help businesses to better manage their inventory, plan their pricing models, advertise effectively, and allocate money and resources.

GPT-4 promises to change the face of modern education at all levels by providing customized learning experiences that can be tailored to individual students. Using data such as student grades, attendance records, and technique inclination, GPT-4 can adapt lesson plans to suit individual needs and abilities to create a more engaging and personalized learning experience for students of all ages.

While virtual cookies and data tracking currently help to enhance the virtual shopping experience, GPT-4 can further these efforts by considering past purchases, web browser history, personal preferences, and more to individually tailor the online shopping experience to an even higher degree. This not only increases sales and brand loyalty for retailers but also improves the overall shopping experience and creates greater ease and convenience for consumers.

Within the healthcare sector, GPT-4 has achieved something no other GPT model can boast: by analyzing medical histories and using clinical imaging, it has been able to successfully suggest relevant diagnoses for patients experiencing healthcare problems, as well as recommend suitable courses of treatment. This can greatly assist medical professionals in providing necessary care, especially in low-income areas where facilities are understaffed.

In industries such as manufacturing and logistics, GPT-4 can help predict when maintenance on equipment will be required in the future using sensor data. This can help these companies prevent expensive downtime and costly repairs by picking up on functional issues early on, further enabling them to enhance productivity and operate more efficiently.

Marketers, social media managers, and content creators can leverage the potential of GPT-4 by using both its knowledge and its language abilities to craft effective marketing strategies, express value propositions, write social media captions, and develop long-form articles and blog posts that sound natural and human-like in their readability.

The fields of journalism, entertainment, and the arts may also be significantly impacted by the features and capacities of GPT-4. Generating new ideas, providing alternative perspectives, and analyzing past practices are all ways in which this newer GPT model can help enhance such livelihoods without making them redundant altogether.

While GPT-3 and GPT-3.5 could also assist users in some of the above ways, its functions have been notably enhanced and fine-tuned in the most recent model. Upgrades in performance, language learning, data tracking, context consideration, and factual accuracy have made the model’s overall usefulness more accessible and relevant to a wider range of professions and business functions.

Limitations of GPT-3 vs. GPT-4

While their lists of abilities are long and impressive, GPT-3 and even GPT-4 are still restricted by certain technological limitations. For instance, human-crafted barriers intended to prevent misuse and the creation of offensive content are useful and necessary, but they can hinder GPT’s ability to answer valid questions.

This is an issue that is relevant to both models. Neither model has the ability to provide information in real-time - something other AI-generated text tools have been able to achieve.

If we look at multilingualism in GPT 3.5 vs. GPT 4, both models are still limited in terms of the services they can provide in languages other than English. Both the translation accuracy and the variety of languages catered to can be considered limitations in this regard, and this continues to be an obstacle for both models.

Even though GPT-4 has introduced the notable ability to consider image input, it still can’t consider audio or video as prompts. Hopefully, this is a capability that developers may consider in the creation of GPT-5, but we can’t yet be sure.

Alternative Tools That Use The GPT Models

You’d be forgiven for assuming the ChatGPT is the most efficient and effective use of the GPT models. In actuality, tools created by other tech competitors using the same technology should, by no means, be underestimated. It could easily be argued that some of them match and even surpass the capabilities of ChatGPT, which has its fair share of limitations and obstacles.


A noteworthy competitor is HIX.AI’s HIX Chat, a tool that supports both GPT-3.5 and GPT-4. This all-in-one chatbot aims to improve communication, give accurate and human-like responses to any query, and build engaging conversations with users. Unlike ChatGPT and many other contemporaries, it has direct web access, allowing it to provide up-to-date information in real-time with no delay or restrictions.

Impressively, it goes beyond the abilities of ChatGPT by reading PDFs and answering questions based on the content, summarizing YouTube videos when a link is provided, and building contextual conversation based on webpage content. It is accessible via a web app as well as a browser extension that can be applied to both Google Chrome and Microsoft Edge.

While no other chatbot can boast the above features, YouChat, Microsoft Bing Chat, and Perplexity Ask are just a few of the most capable chatbot tools that either match or surpass the capabilities of ChatGPT using either GPT-3 or GPT-4.

Future Evolutions of GPT

While OpenAI is presently dedicating most of its resources towards developing the highly-anticipated GPT-5 model, this creation is still in its very early stages and is not yet being actively trained. Since training, trials, and safety measures all need to be implemented before its launch to the public, we can expect to wait a while before this model sees the light of day.

The incorporation of video and audio features as functional input would require using both web coding and video media as training materials for future GPT models, which Altman has alluded to when asked about what we can expect from future versions. The ability to interpret other input outside of written text would be a remarkable breakthrough for LLMs, vastly broadening the scope of what they can deliver and achieve.

Outside of this prediction, little else can be foreseen in terms of what future models may be able to accomplish. As artificial general intelligence is still a largely experimental field, new potential and unexpected capabilities are known to emerge seemingly out of nowhere, so there’s simply no telling what these advanced devices may be able to learn and adapt to in the future.

In Summary

One could incontestably say that both the GPT-3 and GPT-4 LLMs have changed the landscape of artificial general intelligence as we know it, ensuring that AI tools will become a certified aspect of future society.

The advancement of these models have paved the way for holistic, groundbreaking AI platforms such as HIX.AI, a powerful ‘one-stop shop’ that provides a variety of AI-writing solutions that cater to countless consumer needs, all backed and improved by GPT-3.5 and GPT-4.

It might be easy to underestimate just how stark the differences can be, considering the two models were released less than three years apart, but this is purely a testament to how much OpenAI and HIX.AI has achieved in such a short space of time. We can only hope that GPT-5 and future models will continue to pave the way for the standard of AI tools and transform the way we live, work, create, and collaborate.

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