First Impressions of GPT-4: A New Era of Learning with AI

It’s only been a few weeks since OpenAI released GPT-4, and I’ve already been putting it to work. As a ChatGPT Plus subscriber, I’ve gained access to this new model, albeit with frustratingly low rate limits that I hope will improve in the coming weeks.

Despite that, the excitement I am feeling is real. GPT-4 feels like a gear shift, a real turning point, not just in AI capability, but in how I personally approach learning. I’ve been experimenting with Large Language Models (LLMs) for some time now, mainly to augment my learning around technical subjects. With GPT-4, that approach feels more powerful than ever.

GPT-4 as My Personal Tutor

When Khan Academy announced Khanmigo, a tutoring chatbot built on GPT-4—it immediately resonated with me. That’s exactly how I see myself using this technology: not as a replacement for structured learning, but as a companion.

If I’m diving into a new technical skill, I want an AI tutor alongside me that can:

• Break down difficult concepts into digestible explanations
• Provide examples and analogies suited to my level of understanding
• Challenge me with follow-up questions or exercises
• Adjust explanations when I don’t quite get it the first time

I’ve already started experimenting with this style of learning, and GPT-4 is showing itself to be an invaluable study partner.

How GPT-4 Compares to GPT-3.5

To appreciate the leap forward, it’s worth highlighting some of the key differences between GPT-3.5 and GPT-4:

• Multimodality: GPT-3.5 was text-only. GPT-4 can process both text and images, opening up entirely new ways of interacting with information.
• Context Windows: GPT-3.5 had a 16,000-token input limit. GPT-4 pushes that boundary to a staggering 128,000 tokens, making extended conversations, long-form content, and document analysis feasible.
• Scale and Parameters: GPT-3.5 was trained on 175 billion parameters. GPT-4 pushes close to 1 trillion, which translates into stronger reasoning, deeper contextual awareness, and more nuanced outputs.
• General Knowledge: GPT-4 was trained on a broader and more diverse dataset, extending its knowledge through 2023 (depending on the model version). GPT-3.5 is stuck in 2021.
• User Experience: GPT-4 feels more humanlike. It retains context better, provides richer responses, and is significantly more reliable. The trade-off? It runs slower due to the sheer size of the model.
• Accuracy and Safety: GPT-4 achieves human-level performance on professional benchmarks. OpenAI reports 40% higher factual accuracy compared to GPT-3.5, and it is far less likely to generate unsafe or misleading content.

GPT-4’s Multimodal Powers: Seeing and Learning Beyond Text

Another standout leap from GPT‑3.5 to GPT‑4 is its multimodal capability—meaning it can understand and respond to more than just text.

Seeing the Bigger Picture

GPT‑4 is multimodal, able to process both text and image inputs, while generating text responses. This enables it to, for example, interpret diagrams, describe screenshots, or analyse unusual visuals and humour within images. That’s a key difference compared to GPT‑3.5, which remained text-only.

How This Translates Into Better Learning

This capability transforms how you can use GPT‑4 as a tutor.

• Visual problem solving: When tackling network diagrams, flowcharts, or architecture visuals, GPT‑4 can analyse the image and guide you through explanation or troubleshooting.
• Image-based prompts: You can show it a code screenshot or UI design, and get context-sensitive advice or corrections.

These abilities amplify the tutor‑like experience, making learning far more dynamic and accessible—especially when visual materials are part of your workflow.

Learning with LLMs

I’ve always believed the best way to learn is through exploration and curiosity. With LLMs, that process becomes more fluid:

• If I’m stuck on a Linux configuration issue, I can ask for guidance and see multiple approaches.
• If I’m experimenting with a new programming language, I can get quick feedback, code snippets, and debugging help.
• If I’m curious about an area outside my day-to-day work, GPT-4 makes the barrier to entry feel significantly lower.

In short, I’m not outsourcing my learning, I’m amplifying it. GPT-4 feels like the tutor I always wished I had: patient, knowledgeable, and adaptable to my style of learning.

Final Thoughts

The release of GPT-4 feels like the start of a new chapter in how we learn and interact with technology. While the current rate limits are frustrating, they don’t dampen the excitement I feel for what’s possible.

I plan to use GPT-4 as a personal tutor, guiding me through technical skills that will shape my career. The difference now is that the tutor doesn’t just have access to textbooks—it has the collective reasoning ability of a trillion-parameter model.

And that, I think, changes everything.