in Blog

January 04, 2023

ChatGPT vs. GPT-3. The Key Differences

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




6 minutes


ChatGPT and GPT-3 are two of the most advanced language processing models developed by OpenAI, an AI research and development company based in San Francisco, California. Both models utilize deep learning capabilities to produce human-like text, which makes them especially suitable for a wide range of language processing tasks like language translation, summarization, and text generation. However, despite their shared similarities, they have several key differences that make them suitable for different types of tasks.

If you are considering using either language model, but aren’t quite sure which one’s the best fit for your intended purpose, read on for a ChatGPT vs. GPT-3 head-to-head comparison where we evaluate every aspect of the language models, right from their emergence, how they work, and their suitability in different applications.

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ChatGPT vs. GPT-3

Before comparing the differences between the two language models, it is important to know what they are in the first place. ChatGPT [1] is a large language model that was developed based on the GPT-3.5 language model. This incredible model can interact in the form of a conversational dialogue and provide human-like responses.

On the other hand, GPT-3[2] is a neural network machine learning model that can generate literally any type of text by learning from the internet and training data. The language model needs a small amount of input text to produce a large amount of sophisticated and relevant machine-generated text. And, with over 175 billion [3] machine learning parameters, the model is one of the largest neural networks ever produced and outperforms previous models in producing text that appears to be written by a human.

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The differences

Emergence

GPT-3 is the third generation of the GPT series. With over 175 billion parameters, it is significantly larger and more powerful than its predecessors. The language generation model was first announced in June 2020[4] and made publicly available in August of the same year.

According to OpenAI, the GPT series was developed to improve the performance of language generation models by training them on large data sets and then fine-tuning them for specific tasks and applications.

ChatGPT, on the other hand, was developed as a variant of GPT-3.5 for integration into chatbots and other conversational systems. Since its release in September 2020, ChatGPT has proven to be very effective in generating appropriate and coherent responses in a variety of contexts.

Functionality

GPT-3 uses vast amounts of training data and deep learning technology to process up to 500 billion words and numbers to produce human-like responses. Businesses can customize these responses through the model’s simplified API to suit specific needs. The model can also employ predictive analytics to foresee user demands, assess and reply to queries, and give appropriate self-service responses relevant to the conversation’s context.

letters of english alphabet arranged in chaotic order

ChatGPT was developed specifically for chatbot and conversational system applications. The model can answer follow-up questions in long form, admit its mistakes, reject inappropriate suggestions, and dispute unfounded assertions. According to its creator, OpenAI, ChatGPT can effectively respond to various types of written text, including mathematical equations, theoretical essays, and stories through a dialogue model.

Capacity

GPT-3 is huge! The language model has more than 175 billion parameters and can produce 2048-token long-form content. All this requires an enormous storage capacity. The sheer size and abundance of training data make it especially suitable for applications involving more intricate natural language processing.

ChatGPT, on the other hand, is considerably smaller in size compared to GPT-3. However, ChatGPT’s conversational model makes it better suited to real-time chatbot applications since it generates responses faster and more effectively than the former.

Conversational capability

ChatGPT was specially developed for conversation modeling. As such, it excels in producing conversational responses in numerous use cases, including answering questions, creating code, and generating numerous forms of written content, including essays.

desk with computer monitors displaying code

However, GPT-3’s superior size and resources enable it to perform a wider range of functions, including text generation, machine translation, and question-answering. It also has a general-purpose design that gives it unmatched business application capabilities like relieving the technical debt of legacy code, improving search and product discovery, and handling customer service conversations in real time.

Output quality

The output quality of ChatGPT and GPT-3 ultimately comes down to the specific task and use case. In general, ChatGPT generates higher-quality responses to user input in a conversational context because it is specifically designed for chatbot applications and has been fine-tuned on a dataset of conversations specifically designed for chatbot applications.

However, as language generation models, the quality of their output ultimately depends on the quality of the input they receive. The response may be flawed or of lower quality, especially if a user uses poorly structured, ambiguous, or otherwise difficult-to-understand input. Additionally, both models are subject to the limitations of machine learning technologies and may produce responses that are entirely coherent or accurate.

ChatGPT vs. GPT-3 Differences: Final Thoughts

Since their development, ChatGPT and GPT-3 have been making waves in the business community as well as the general population. Their effectiveness in generating human-like responses makes them suitable for a wide variety of applications.

However, despite their shared similarity as large language generation models, their unique configurations limit their use cases, thus necessitating the need to only pick one depending on specific use cases. Generally, ChatGPT is more suited to chatbot and conversation; applications, while the latter is better suited to tasks that require more intricate natural language processing.

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References

[1] Wikipedia.org. ChatGPT. URL: https://en.wikipedia.org/wiki/ChatGPT. Accessed December 30, 2022
[2] Wikipedia.org. GPT-3. URL: https://en.wikipedia.org/wiki/GPT-3. Accessed December 30, 2022
[3] Developer.nvidia.com. Open AI Presents GPT-3, a 175 Billion Parameters Language Model. URL:  https://developer.nvidia.com/blog/openai-presents-gpt-3-a-175-billion-parameters-language-model/. Accessed December 30, 2022
[4] Wikipedia.org. GPT-3. URL: https://bit.ly/3IEmhR9. Accessed December 30, 2022



Category:


Generative AI