Just a few months ago, ChatGPT was virtually unknown. Today, its absence would pose a problem for countless users.
This innovative technology has saved hundreds of hours, boosted work speed, and unlocked new levels of creativity. However, even the most revolutionary inventions require ongoing improvement. And that’s precisely why GPT4 was developed as the successor to the highly acclaimed GPT 3.5.
The GPT4 iteration sets new standards for text production, language comprehension, and problem-solving. Despite its limitations, this technology has already made a profound impact on numerous domains and applications.
In this article, we’ll explore the fascinating world of GPT 4, how it integrates with ChatGPT, and the ways in which it is transforming generative AI.
Why GPT4 is a game-changer in the world of generative AI?
GPT4 is powerful multimodal model and has the ability to process both text and images, and achieves human-level results across a variety of academic and professional standards.
To put it simply, GPT4 scored among the top 10% in a bar exam simulation, whereas GPT 3.5 was among the bottom 10%. Although the differences between GPT 3.5 and GPT-4 may not be immediately apparent in casual conversation, GPT-4 excels as task complexity increases.
Compared to its predecessor, GPT-4 is more dependable, creative, and capable of handling much more complex commands. OpenAI tested these models against a wide range of criteria, including tests originally designed for humans, in order to fully understand the differences. They used the most recent publicly available exams, as well as practical tests, and did not, of course, subject the models to any specialized preparation before the tests. It’s clear that GPT-4 has greater cognitive abilities than GPT 3.5.
While it has always performed well on numerous exams, both before and after, its performance has significantly improved on tests requiring more complex logic and reasoning.
What are the differences between GPT4 and GPT 3.5?
Interestingly, OpenAI has been relatively tight-lipped about the specifics of GPT-4’s size and exact numbers that could explain why it outperforms its predecessor. The company has continued to emphasize improvements in test results but has said nothing about the underlying assumptions of the new model. This secrecy could be linked to the highly competitive nature of the AI industry, where every technological advancement can have a significant impact.
In a field that evolves as rapidly as technology, OpenAI may be securing its innovations to maintain a competitive advantage. The distinctions between GPT 3.5 and GPT-4 will likely be better understood over time, shedding light on the causes of GPT-4’s improved performance.
We wanted to highlight some striking contrasts we discovered when we asked ChatGPT the same question using two different models (GPT-4 and GPT 3.5). We tested its ability to reason, use logic, and communicate. Here are some of the results we obtained:
GPT4 can process eight times more words than GPT3
GPT4 has way bigger word limit than GPT3 , it can process up to 25,000 words, which is eight times more than its predecessor. This amazing ability allows Chat GPT-4 to handle larger documents and perform more efficiently in certain work environments. It also outperforms GPT3 by up to 16% on common machine learning benchmarks and excels in handling multilingual tasks, making it more accessible to non-English speakers.
How does GPT4 use difficult vocabulary and syntax?
We used the following prompt: “Describe as precisely as possible what an apple is. Use interesting, original, and enticing language to describe everything that might make a consumer want to eat a apple. In one paragraph.”
Some of the most effective words and descriptions were highlighted. It’s clear that GPT-4 did a better job of drawing readers in.
How does GPT4 reason and explain concepts?
We used the following prompt: “Explain to me in one paragraph the usefulness of the LED light technology, but do it as if you were addressing a middle school class and using an analogy.”
Once again, GPT-4 outperforms its predecessor. Its analogy is much subtler and tailored to the target audience.
The Cost of using GPT4 vs GPT3
Undoubtedly, this advanced technology comes with a price tag. GPT-3 models range in cost from $0.0004 to $0.02 for every 1,000 tokens, while the latest GPT-3.5-Turbo is ten times more affordable ($0.002 per 1,000 tokens) compared to the high-end GPT Davinci model.
However, GPT-4’s pricing makes it clear that accessing cutting-edge models requires a higher investment. For GPT-4 with an 8K context window, the price is $0.03 per 1,000 prompt tokens and $0.06 per 1,000 completion tokens. In contrast, the 32K context window variant costs $0.06 per 1,000 prompt tokens and $0.12 per 1,000 completion tokens. Consequently, processing 100,000 requests with an average length of 1,500 prompt tokens and 500 completion tokens would amount to $7,500 for the 8K context window and $15,000 for the 32K context window, compared to $4,000 with text-davinci-003 and $400 with GPT-3.5-Turbo.
Not only is GPT-4 more expensive, but its pricing structure is also more complex due to the different costs for prompt (input) and completion (output) tokens. As we learned from the GPT-3 pricing experiment, estimating token usage is challenging due to the low correlation between input and output lengths. The higher cost of completion tokens makes GPT-4’s overall pricing even more unpredictable.
Additional thoughts on GPT4
The level of improvement with GPT-4 has been significant, but what makes it so much better than its predecessor remains unknown. It performs exceptionally well across a wide range of workloads and will continue to make waves in many industries, driving new developments over time.
It’s essential to recognize the progress that this iteration represents for artificial intelligence. It’s not just a small improvement, but a massive leap forward that demonstrates the continuously growing possibilities of AI. Undoubtedly, the future of artificial intelligence is more promising than ever, with GPT-4 at the forefront.
However, its release raises moral and ethical questions that must be taken into account. The risk of misuse or abuse increases as AI becomes more sophisticated and powerful. This could lead to the spread of misinformation, the production of deepfakes, or the amplification of destructive content.
As AI systems improve their ability to understand human behavior and language, concerns about surveillance and privacy protection may begin to emerge. In addition to the attention of tool developers, addressing these concerns requires the participation of users and society as a whole.
OpenAI has acknowledged the importance of addressing these issues and is committed to studying and implementing security measures to reduce risks. Even Sam Altman, the CEO of OpenAI, believes that AI should be further regulated. It’s somewhat alarming to hear the CEO of one of the emerging companies in the sector publicly suggest that the products they produce should be subject to regulation.
