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AI’s Unequal Playground: How the New ChatGPT-4 Paywall Intensifies the Performance Gap

In an era where artificial intelligence (AI) is radically transforming the way we communicate, collaborate, and innovate, OpenAI consistently delivers remarkable breakthroughs in natural language processing. The latest sensation in the AI world is OpenAI’s GPT-4, now available exclusively to ChatGPT Plus paid subscribers.

Introducing ChatGPT-4: The cutting-edge AI that’s redefining possibilities!

But does it truly deliver? Is ChatGPT-4 the anticipated leap forward or merely an incremental upgrade, akin to transitioning from an iPhone 13 to an iPhone 14?

Let’s explore the distinctions between the free and premium GPT models on OpenAI’s ChatGPT platform, examining model outputs, and their implications for enhancing academic performance and the accessibility of these powerful AI tools.

OpenAI’s ChatGPT is currently running three models: Default, Legacy, and GPT-4.

Let’s compare them.

Overview of performance claims

ChatGPT Default Model:

Running GPT-3.5

ChatGPT Legacy Model:

Running GPT-3.5

NEW ChatGPT GPT-4 Model:

Limitations Remain

Despite some expectations for the new model, GPT-4 is still limited in its ability to know what’s currently going on in the world.

Putting GPT-4 to the test

Let’s test out the three models and see how their output varies.

Using the prompt “Explain the difference between auto-ethnography and visual-ethnography in less than 200 words”, I received the following outputs from the three models:

ChatGPT Default Model Sample Output:

Auto-ethnography is a qualitative research method that involves studying and analyzing one’s own experiences and personal reflections in a particular social or cultural context. The researcher uses their own personal experiences and observations to understand the culture or social phenomenon under investigation. This method emphasizes the researcher’s personal perspective and subjective interpretation of the data.

Visual ethnography, on the other hand, is a qualitative research method that uses visual media such as photographs, videos, and other forms of visual representation to understand cultural or social phenomena. The researcher collects and analyzes visual data in order to gain insights into the culture or social context under investigation. This method emphasizes the use of visual media as a means of understanding and interpreting cultural practices and beliefs.

In summary, auto-ethnography focuses on the researcher’s personal experiences and reflections, while visual ethnography uses visual media to understand cultural or social phenomena.


ChatGPT Legacy Model Sample Output:

Auto-ethnography is a qualitative research method that involves the researcher reflecting on their personal experiences and cultural identity in order to understand and explore a cultural group or phenomenon. The goal of auto-ethnography is to use the researcher’s own experiences as a way to gain insight into a particular culture, and to challenge traditional ways of understanding and representing that culture.

Visual-ethnography, on the other hand, is a type of ethnographic research that incorporates visual media, such as photographs, videos, and illustrations, as a means of representing and exploring a particular cultural group or phenomenon. Visual-ethnography emphasizes the role of visual media in representing cultural experiences, and aims to use visual media to create a more nuanced and emic understanding of a particular culture.

In summary, auto-ethnography focuses on the researcher’s personal experiences and cultural identity as a way to understand a cultural group, while visual-ethnography emphasizes the use of visual media to represent and explore cultural experiences. Both methods aim to challenge traditional representations of culture and to create a more nuanced and emic understanding of a particular cultural group.


ChatGPT GPT-4 Model Sample Output:

Auto-ethnography and visual-ethnography are two distinct approaches to ethnographic research, which focuses on understanding cultural practices, values, and beliefs within a specific group or community.

Auto-ethnography is a self-reflective, introspective methodology where the researcher uses their own experiences, emotions, and personal narrative to explore and analyze their cultural identity and belonging. By incorporating their subjective lens, auto-ethnographers highlight the interconnection between the self and the cultural context, emphasizing the researcher’s positionality and vulnerability.

Visual-ethnography, on the other hand, employs visual media (such as photographs, videos, and film) as primary data sources to document and analyze cultural phenomena. This approach allows researchers to capture non-verbal communication, spatial relationships, and social interactions, providing a rich and immersive understanding of the studied community. Visual-ethnographers rely on visual materials to enhance their observations, facilitate communication, and represent findings more vividly and engagingly.

In summary, auto-ethnography emphasizes the researcher’s personal experiences and reflections as a central aspect of the study, whereas visual-ethnography focuses on using visual materials to document, explore, and present a cultural context.


Notice a difference? 

I asked ChatGPT (GPT-4) to analyse the three texts to compare the sophistication of language use, concision, and reasoning. Here’s its critique:

Comparing the three texts in terms of sophistication of language use, conciseness, and reasoning:

The sophistication of language use:

  • Default: The language used is clear and straightforward, but it lacks some depth and nuance in explaining the differences between the two methodologies.
  • Legacy: The language used is more sophisticated, providing a more detailed explanation of the concepts and goals of the two methodologies.
  • GPT-4: The language used is the most sophisticated of the three, offering a deeper and more nuanced understanding of the two methodologies, highlighting aspects such as the researcher’s positionality and vulnerability.


  • Default: This text is relatively concise, providing a brief explanation and comparison of the two methodologies.
  • Legacy: While slightly longer than the Default Model’s text, this text remains concise and offers more detail in its explanation of the two methodologies.
  • GPT-4: Although the most detailed of the three, this text is still relatively concise, considering the depth of information provided.


  • Default: The reasoning in this text is simple and straightforward, offering a clear comparison between the two methodologies but lacking a deeper analysis.
  • Legacy: The reasoning in this text is more robust, providing a clearer understanding of the goals and differences between the two methodologies and their contribution to challenging traditional representations of culture.
  • GPT-4: The reasoning in this text is the strongest, offering a well-rounded and insightful analysis of the two methodologies, including the importance of the researcher’s positionality and the use of visual materials to document and present cultural context.

In conclusion, GPT-4 demonstrates the highest level of sophistication in language use, maintains conciseness while providing greater depth, and offers the strongest reasoning. The Legacy Model follows, with increased sophistication and reasoning compared to the Default Model. The Default Model, while clear and concise, offers the least sophisticated language use and reasoning among the three texts.


In conclusion, as the latest AI model continues to be accessible only through a paywall, the enhancements it brings appear to further amplify the performance edge available to students and researchers who have the financial means to access it. 

Ultimately, this highlights not only the increasing gap between those who can afford these state-of-the-art tools and those who cannot but also the divide between individuals who adopt the technology to enhance their capabilities and those who continue to rely on conventional methods.

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