OpenAI has achieved a milestone in the seemingly infinite world of artificial intelligence. It has transcended the boundaries of natural language, its understanding, and the communication based on it. The two models, GPT-3.5 and GPT-4, are outstanding in their respective features and continuously updated with progress. The AI community is excited and curious to work with these models.
Even though both models belong to the same family, they vary in many aspects, such as their training data, abilities, model size, and access. We will compare these two models in this blog post and discuss their similarities and differences.
GPT-3.5 and GPT-4 are natural language models developed by OpenAI, a research organization dedicated to creating and ensuring the safe use of artificial intelligence. These models are used by various applications, such as ChatGPT and Bing AI chat, to generate human-like text and interact with users.
Both the models are based on the Generative Pre-trained Transformer architecture, which uses a large neural network to learn from a massive amount of text data to produce coherent and relevant responses to any given prompt. The models are pre-trained on a large corpus of text from the internet, such as Wikipedia, news articles, books, social media posts, etc., and then fine-tuned for specific tasks and domains.
GPT-3.5 is short for ‘Generative Pre-trained Transformer 3.5’. It is a follow-up to GPT-3, which feature optimized models customized for text-completion and code-completion actions. GPT-3.5-Turbo was released in March 2023, further amplifying interest with its improved performance and cost-effectiveness.
GPT-3.5 is extraordinary when it comes to understanding and generating text, answering questions, performing language translations, as well as engaging in conversations with users. Its adaptability and diverse set of applications make it extremely popular in various industries, including customer service and chatbots, where it acts as a powerful means for automating and enhancing various processes. Even with all the amazing capabilities, it still has some limitations, such as some challenges in maintaining rationality during deep conversations and an inability to understand sarcasm or humor.
Now coming to GPT-4; ever since its launch on March 14, 2023, it has been recognized as the most remarkable language model of OpenAI. It offers unparalleled abilities with various applications, and they keep on increasing with each new update and improvement.
It represents the next step in natural language processing technology, building upon the success and foundation of its predecessor. It keeps on enhancing the boundaries of AI capabilities even further.
One of the most distinguished differences between GPT-4 and earlier models is its massive scale, with countless parameters, making it one of the largest language models ever created. This considerable increase in size makes it equipped with a unique capacity for information retention. It also enables it to maintain consistency and significance in deep, meaningful, longer conversations and complex communications.
It also overcomes the limitation of its predecessor by understanding humor, sarcasm, and vague prompts. This boosted comprehension of framework and subtleties in language results in more meaningful, contextually appropriate, and amusing replies, creating a more pleasant and engaging user experience.
Both models can be used for a variety of natural language processing tasks, such as text generation, summarization, translation, question answering, sentiment analysis, etc. However, depending on your needs and preferences, you may choose one model over the other.
One way to use them is through the OpenAI API, which allows you to access the models programmatically using Python or other languages. You need to create a developer account with OpenAI and obtain a secret key to use the API. You can then send requests to the API with your prompt and parameters, and receive responses from the models.
Another way to use GPT-3.5 and GPT-4 is through ChatGPT, a web-based application that lets you chat with the models conversationally. You can choose between different personalities and domains for the models, such as friendly, professional, funny, etc. You can also adjust the temperature and top-p parameters to control the randomness and diversity of the responses.
As we have seen, both are powerful natural language models, but they are not the same. They have some important differences that affect their performance, functionality, and usability.
Both models are designed to interact with users through natural language, understand the requirements, and generate coherent responses accordingly to satisfy the users’ needs. These AI models are built on the foundation of the Generative Pre-trained Transformer architecture but differ significantly in their technical specifications.
GPT-3.5 is a text-to-text-based model with restricted contextual memory and is equipped with a few abilities. On the contrary, GPT-4 goes beyond in several ways. It is a revolutionary Multi-Modal Model, equipped with phenomenal capacity, making it considerably more powerful. The increased number of parameters empowers it to comprehend difficult responsibilities and language outlines with unrivaled aptitude, setting it apart from its predecessor.
GPT-4 has an extensive parameter count and enhanced computational capabilities. Parameters are the components that embody the knowledge the model acquires from the training data. The more parameters a model has, the more it can learn and remember. With its (rumored) 1 trillion parameters, GPT-4 outperforms GPT-3.5, which comprises 175 billion parameters.
Memory is the most fundamental feature of language models. A model can store and recall information from previous inputs or outputs. The more memory a model has, the more it can maintain coherence and relevance in longer texts or conversations.
Compared to GPT-3.5’s 4000 token limit, GPT-4 has a context length of 8,192 tokens, but they are also allowing 32,768 tokens which are equivalent to 50 pages!
Both AI models have many similarities and differences but the most significant of them all is their distinction in their communication abilities. GPT-3.5 is a text-to-text model, which means it can only accept and generate plain text input and output. GPT-4 is a multi-modal model, which means it can accept and generate both text and image input and output. This makes it more versatile and capable of processing different types of data
Steerability is the ability of a model to be controlled and guided by the user to produce the desired output. GPT-4 is more steerable, which means it can be more easily influenced by the user’s preferences, instructions, or corrections
GPT-4 has a novel approach to AI customization, departing from the fixed tone, style, and verbosity of the classic ChatGPT personality (currently powered by GPT-3.5 for free users). Developers have the power to define the style and task of their AI by providing specific instructions in the “system” message. With this capability, API users can extensively customize their users’ experience, while still operating within predefined boundaries.
Connectivity is the ability of a model to interact with other applications and services more seamlessly. With GPT-3.5, you are on your own to create connectivity for your model and make it interact with the Internet. With GPT-4, OpenAI has introduced Plugins for ChatGPT that allow it to utilize various services and generate much more fine-tuned responses.
The power of AI models is directly proportional to the strength of the data it is built on. Both the models source their power and capabilities from the diverse data collected from multiple sources.
GPT-3.5 was built on a widespread amount of text data including blogs, books, social media posts, articles, and websites. Even though it showcased remarkable language generation competencies, its dataset was inadequate in comparison to its successor.
GPT-4, on the other hand, incorporates a substantial increase in its training data. In the advanced version, OpenAI comprehends a larger and more diverse dataset, integrating a broader range of data including texts, images, and multimedia content. This improvisation has meaningfully augmented the model’s understanding of language and its aptitude to process diverse inputs.
GPT-4 can easily manage extensive pieces of text in a single API request compared to GPT-3.5. This amplified context window helps in maintaining a better understanding of longer conversations and produces more lucid responses.
For example: Suppose a user asks both models the question:
“I am planning a vacation in Los Angeles this new year.” This question is followed by another one, “What are the most popular places in the city?”
– GPT-3.5 might face restrictions in retaining the context of the previous statement and instead provide generic tourist spots in Italy.
– GPT-4, with its improvised capabilities for context, can retain information about the user’s travel plans and offer more tailored and relevant recommendations.
Both models are capable of performing a wide range of natural language processing tasks, such as text generation, summarization, translation, question answering, sentiment analysis, etc. However, GPT-4 has some enhanced capabilities that make it superior to GPT-3.5 in some aspects. Here are some of the key capabilities of the models according to the following aspects:
Language is dynamic and diverse, influenced by many factors. People may speak the same language differently, creating dialects. Language also expresses emotions, which affect communication.
Both AI models are natural language models that can generate and interact with text. However, GPT-4 is better at understanding and generating different dialects and emotions. For example:
These examples show how GPT-4 can communicate more naturally and engagingly with different dialects and emotions. This can be useful for many applications, such as chatbots, social media, education, entertainment, etc.
The GPT-4 model stands out with its exceptional capability to deliver more imaginative responses to prompts. It can also maintain coherence and relevance in longer texts or conversations better than its earlier iteration.
GPT-4 can accept images as part of a prompt, whereas GPT-3.5 only processes plain text input. It can also generate images based on text descriptions or modify existing images according to instructions.
As you can see in the screenshot, the model was able to understand the image and make sense of the joke communicated with that image.
Not only this, but in the GPT-4 announcement video, OpenAI also showcased a few other uses, like adding a picture of your fridge items; it can suggest various dish recipes that you can make from whatever you have available in your kitchen. Also, by just adding a rough sketch of your website idea, it can turn it into a working code!
GPT-4 can solve complex problems that require logic, reasoning, or mathematical skills better than GPT-3.5. It can also pass exams that would stump most humans, such as complicated legal Bar exams.
It achieved an impressive score in the 88th percentile on the LSATs and even surpassed that achievement with a score in the 90th percentile on the Bar exam. This capability is only going to increase, and GPT-4 demonstrates significant progress over earlier models, which barely passed exams with a passing score.
Such capabilities will become increasingly useful, especially in areas where extensive learning, reading, and research are involved. These applications will be applied to popular learning platforms, test generation for students, summarizing long articles and study papers, and more.
GPT-4 has also acquired the capability to navigate humor. It can comprehend it and generate delightful responses accordingly. This feature in particular sets it apart from its predecessor, which at times resisted developing an understanding of sarcasm.
With GPT-4, users can enjoy the benefit of more contextually appropriate and witty responses. Whether it’s a witty remark or a sarcastic comment, it analyses it and consistently delivers responses. This makes the whole experience seemingly more humane and engaging.
Ensuring the delivery of precise and reliable information is a crucial aspect of any language model. In this regard, GPT-4 holds a remarkable advantage, as it can synthesize information from multiple sources to address complex queries, while GPT-3.5 might encounter difficulties in connecting disparate pieces of information.
For instance, if someone inquires about how the decrease in bee populations affects worldwide agriculture, GPT-4 showcases its abilities by providing a comprehensive and intricate answer, while also citing multiple studies and sources. In addition, it elaborates on the reasons and outcomes of this occurrence while suggesting possible remedies or steps to be taken. On the other hand, GPT-3.5 may provide a less detailed or unclear response, and it could even make mistakes or offer inconsistent data.
Another feature that makes GPT-4 more accurate and trustworthy is its ability to properly cite sources when generating text. Unlike its predecessor, it includes a citation mechanism that allows it to reference the sources it has used when creating content, making it easier for readers to verify the information presented. This also helps to avoid plagiarism and enhance credibility.
These examples show how GPT-4 can provide more accurate and reliable information, which can be useful for various applications, such as research, education, journalism, etc.
GPT-4 can perform advanced programming tasks, such as writing code, debugging errors, and creating applications. It can also integrate with other applications and services more seamlessly. While GPT-3.5 is powerful, GPT-4 brings even more capable programming and debugging skills.
Also read: Understanding the workings of Deep Q-learning
GPT-4 has revolutionized the entire platform for multi-faceted interactions. It has set higher standards for content generation and user engagement. Its predecessor faced many restrictions in consistently maintaining an understanding of extensive user queries. GPT-4 has been upgraded to surpass this type of limitation. It has an expanded context window, which enables it to take hold of the flow as well as the history of extended conversation with excellent depth.
With this enhancement, GPT-4 can accurately comprehend extensive conversations and generate meaningful responses. It can make sense of complicated interactions with tact and generate sustained responses. It can preserve critical information blocks, hence resulting in immersive communication. Hence, providing a greater sense of satisfaction to the user.
GPT-4 ensures a smooth and natural conversational flow, transforming the way AI interactions used to happen. Unlike its predecessor, whose responses felt artificial and robotic, GPT-4 provides a more human-to-human feel in terms of interactions that users experience.
The continuous flow of conversation creates a more meaningful and engaging user experience, where conversations with it feel authentic and real. This improvisation makes it unique and enhances its overall utilizing capability.
GPT-4 also uplifts the creative capability with its incorporation of advanced contextual prompts. Users are able to unlock the ingenious potential of the AI framework by providing detailed instructions and creative prompts. GPT-4 can determine the creative quotient and generate innovative solutions. This represents a paradigm shift in terms of storytelling and artistic pursuits. Users can increase their own capabilities with a multitude of ideas that this AI model suggests and thereby feeling more fulfilled while being on the path of creativity.
Through these notable developments, GPT-4 has become a pillar in the AI language models and has set new standards for context understanding, creativity, and personalization.
Both models with their unique capabilities have found their utilization in diverse industries. These models have transformed the AI interaction completely right from the understanding of context, to generating meaningful responses and rendering customer support.
GPT-4 especially has opened an array of possibilities with its proficiency. Its progressive reasoning and intricate problem-solving skills find practical applications in areas that require high-level cognitive tasks. It has become a powerful tool for web developers as they provide expertise in writing code and debugging error positions. It can also be utilized in educational platforms, as it offers custom learning experiences, summarizes lengthy articles, and generates challenging test questions. Moreover, its ability to generate information from numerous sources ensures that it excels in research, journalism, and content creation, strengthening precision and credibility.
Both GPT-3.5 and GPT-4 have some risks associated with their use, such as generating inappropriate or biased responses, plagiarizing content, or being misused for malicious purposes. However, GPT-4 has built-in mechanisms to mitigate these risks, such as filtering out harmful content, providing disclaimers or citations, and requiring human feedback or supervision.
GPT-4 is committed to precision and coherence. It is reflected truly in its training process, resulting in an obvious reduction in generating incorrect or incoherent responses compared to GPT-3.5. It has a greater sense of understanding of user queries and provides relevant and meaningful responses without generating incorrect responses. It provides authentic responses which are factually correct and can comprehend information in terms of whether it is appropriate or not.
The distinguished framework of GPT-4 ensures a more reliable AI experience, which generates more trustworthy responses and users can receive authentic information and insights. Users have a meaningful experience, and it finds its utilization in diverse sectors; thus, forming a trust bond with the user.
GPT-4’s improvisation is a demonstration of the advancement of AI technology. It is capable to handle larger context lengths and complicated tasks. This makes it an ideal fit for large-scale projects and widespread user interactions. As industries are becoming comfortable with AI integration, its scalability ensures it can cater to a wider array of use cases.
With improved capabilities and performance, the costing bit of GPT-4’s access has also increased. However, it cannot become a restriction since any organization will weigh its advantages against its pricing model to determine the most suitable fit for its needs.
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GPT-4 is now widely available, and OpenAI has announced that older models will slowly be deprecated. This is great news for anyone who wants to integrate a powerful AI model with their product; now is the right time.
OpenAI has now made GPT-4 APIs available for the general public. OpenAI has decided to move from text completion to chat completion, as chat completion constitutes 97% of API usage.
ChatGPT Plus is the premium version, the web-based application that allows you to have conversational chats with both models. To access ChatGPT Plus, you need to pay $20 per month, which gives you priority access to ChatGPT, unlimited prompts, and the option to use GPT-4 as the default model.
Another way to access GPT-4 is through Bing Chat. Bing Chat is a new feature of the Bing search engine that allows you to chat directly from the search results page. You can ask questions, get answers, or have a casual conversation with GPT-4, which is integrated with Bing’s knowledge graph and other services. Bing Chat is currently available to select users in specific regions.
Some other applications use GPT-4 or its variants for specific purposes, such as Microsoft 365 Copilot, GitHub CopilotX for writing code, DALL•E for generating images, or Quora’s Poe Subscriptions for writing content. However, these applications may not give you full access or control over GPT-4’s capabilities.
GPT-3.5 and GPT-4 are both amazing natural language models that can generate human-like text and interact with users. However, they have some significant differences that make them suitable for various use cases and scenarios. GPT-4 is larger, smarter, more diverse, more steerable, and more connected than its predecessor, but it is also slower, more expensive, and less accessible. Depending on your needs and preferences, you may choose to use either of them for your natural language processing tasks.
We hope this blog post has answered some of your questions about AI models. If you want to try out these models yourself, you can use ChatGPT or its premium version to chat with them in a conversational manner or use OpenAI API to access them programmatically using Python or other languages.
If you liked reading this blog post, then also read our blog on how you can streamline token usage in contextual conversations.
If you need help in integrating the capabilities of these AI models into your software product and applications, then reach out to us for help.
