Technical Content Writer at almaBetter
In this article, we will compare Chat GPT 3 vs. 4, two prominent models/tools from OpenAI, to understand their similarities, differences, and their potential.
We all have used Google in our life. But have you ever wondered how ChatGPT is different from Google? In the world of Artificial Intelligence, OpenAI's language models have been making waves with their remarkable capabilities. The recent release of GPT-4, the most advanced system developed by OpenAI, has garnered significant attention. With enhanced problem-solving abilities and broader general knowledge, GPT-4 promises to deliver greater accuracy in tackling complex tasks. We have already discussed about ChatGPT vs Google before, now in this article, we will compare Chat GPT-4 vs. Chat GPT-3.5, two prominent models from OpenAI, to understand their similarities, difference between GPT 3 and GPT 4, and the potential impact they can have on various applications.
Chat GPT-3.5, part of the GPT-3 series, is a language model developed by OpenAI. It gained widespread recognition for its exceptional language generation capabilities. Released in March 2023, Chat GPT-3.5 quickly piqued interest in the AI community. However, it was soon overshadowed by the arrival of Chat GPT-4, which stole the spotlight.
Chat GPT-4, introduced in March 2023, represents a significant leap forward in deep learning. OpenAI positions it as a groundbreaking milestone in language model development. This new version of GPT boasts several notable enhancements compared to its predecessors, including GPT-3 and GPT-3.5. Let's delve into the key differences between these models and explore the unique features of Chat GPT-4.
One of the most significant distinctions between GPT 4 vs GPT 3 lies in their capabilities. Chat GPT-4 is touted as more reliable, creative, collaborative, and capable of handling nuanced instructions with greater precision than Chat GPT-3.5. OpenAI developers conducted various tests, including simulated exams initially designed for humans, to demonstrate the superiority of Chat GPT-4.
Chat GPT-3 scored poorly on the AP Calculus BC exam in these tests, receiving a score of only 1 out of 5. However, Chat GPT-4 achieved an impressive score of 4, showcasing its improved performance in calculus. Similarly, Chat GPT-4 outperformed Chat GPT-3.5 in a simulated bar exam in the Chat GPT-4 vs. Chat GPT-3.5 challenge, scoring in the top 10% of test takers compared to the bottom 10% for Chat GPT-3.5.
Chat GPT-4 exhibits remarkable proficiency in multiple languages. While its predecessors, including Chat GPT-4 vs GPT-3 or GPT-3 vs GPT-4, demonstrated high English proficiency, Chat GPT-4 takes it further. With an accuracy of over 85% in English, Chat GPT-4 even surpasses its ancestor's English language proficiency. Additionally, Chat GPT-4 showcases its ability to communicate effectively in 25 other languages, such as Mandarin, Polish, and Swahili. This multilingual competence positions Chat GPT-4 as a versatile language model that can cater to a more diverse user base.
Context length, referring to the number of tokens a model can process in a single API request, is an essential parameter in language models. Chat GPT-4 introduces two variants: Chat GPT-4-8K and Chat GPT-4-32K. These variants differ in the size of their context windows, enabling Chat GPT-4 to process longer pieces of text within a single request.
The Chat GPT-4-8K variant has a context length of 8,192 tokens, while the Chat GPT-4-32K variant can handle an impressive 32,768 tokens. In comparison, Chat GPT-3 had a maximum context length of 4,096 tokens. This expanded context length empowers developers and users to work with more extensive documents, analyze and summarize larger reports, and maintain contextual understanding during conversations.
Chat GPT 3 vs ChatGPT 4: Unlike its predecessors, Chat GPT-4 introduces a significant shift in input types. While Chat GPT-3 and Chat GPT-3.5 were limited to text-based inputs, Chat GPT-4 welcomes including images as an additional input type. This means that Chat GPT-4 can generate text outputs based on combined text and image inputs.
The implications of this shift are substantial. Chat GPT-4 can generate captions for images, classify visible elements within images, and even analyze the content of images. For instance, it can analyze graphs, explain memes, and summarize documents consisting of both text and images. This newfound ability to process pictures expands the potential use cases for Chat GPT-4, from academic research to personal training or shopping assistants. It should be noted. However, that image inputs are still in the research preview stage and not yet publicly available.
Chat GPT-4 introduces an innovative feature that allows users to define the model's tone, style, and behavior through system messages. These system messages provide instructions on an API level, enabling users to guide the model's responses and shape its interactions. Users can establish the desired tone and behavior for Chat GPT-4 by including system messages, creating a more customizable and consistent experience.
The introduction of system messages builds upon a similar capability introduced in Chat GPT-3.5-Turbo, where users could define the model's role through system prompts. Many professionals such as developers can utilize specific ChatGPT prompts for developers and make their work easy. However, Chat GPT-4 takes this concept further, allowing for more precise and refined control over the model's behavior, making it more adaptable to specific applications and brand guidelines.
While the advancements in Chat GPT-4 are impressive, they come at a cost. Pricing for Chat GPT-4 is significantly higher compared to its predecessors. Chat GPT-3 models ranged from $0.0004 to $0.02 per 1,000 tokens, with Chat GPT-3.5-Turbo priced at $0.002 per 1,000. In contrast, Chat GPT-4 comes with a higher price tag.
The Chat GPT-4-8K variant costs $0.03 per 1,000 prompt tokens and $0.06 per 1,000 completion tokens. The Chat GPT-4-32K variant is even pricier, costing $0.06 per 1,000 prompt tokens and $0.12 per 1,000 completion tokens. These increased costs, coupled with the complexity of calculating token usage, make the cost of using Chat GPT-4 less predictable and potentially prohibitive for some users.
While Chat GPT-4 offers enhanced control over its behavior through system messages, fine-tuning remains valuable for customizing models to specific applications. Fine-tuning involves training a model on extensive examples beyond what the prompt can accommodate, resulting in a more tailored and application-specific model.
However, it's important to note that, as of now, fine-tuning is only available for the original GPT-3 base models, such as Davinci, Curie, Ada, and Babbage. The ability to fine-tune these models allows users to reduce costs, improve latency, and achieve a more personalized AI experience. Although not yet available for Chat GPT-4, fine-tuning remains a powerful tool for customization in other OpenAI models.
While Chat GPT-4 showcases remarkable advancements, it is essential to acknowledge its limitations. OpenAI CEO, Greg Brockman, emphasized that despite the model's advancements, it is not an entirely reliable system. Chat GPT-4, like its predecessors, can still generate errors, make reasoning mistakes, and provide inaccurate information. It is crucial to exercise caution and avoid relying on Chat GPT-4 in areas where errors could have significant consequences.
Furthermore, it is worth noting that OpenAI does not currently possess actual Artificial General Intelligence (AGI). While impressive, Chat GPT-4 capabilities should be viewed in the context of AI development and the ongoing pursuit of AGI.
Chat GPT-4 represents a significant leap forward in language model development. With improved capabilities, multilingual proficiency, expanded context length, and the ability to process images, Chat GPT-4 opens up exciting possibilities in various domains. However, the higher cost and inherent limitations emphasize the importance of carefully considering the specific use case before adopting Chat GPT-4.
As the AI landscape continues to evolve, the advancements made by Chat GPT-4 pave the way for future innovations and improvements. While Chat GPT-4 vs. Chat GPT-3.5 remain viable options for many applications, Chat GPT-4 pushes the boundaries of what language models can achieve. As developers and users explore the unique features of Chat GPT-4, they will undoubtedly uncover new possibilities and unlock the full potential of AI-powered communication.
If you're considering integrating Chat GPT models into your project, it is crucial to assess your specific requirements and consult with experts to ensure the best fit and maximize your chances of success. By learning ChatGPT, you can unlock the potential to simplify your work and enhance productivity significantly.