Chat GPT 3 vs Chat GPT 4 | What is GPT-4 - When will it be released?
What is GPT-4?
Check Out: What is the reason to Make ChatGPT-4?
When Will GPT 4 Be Released?
GPT-4 vs. GPT-3 model’s capabilities
We proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative.
(source:OpenAI)
The results are stunning!
While GPT-3 scored only 1 out of 5 on the AP Calculus BC exam, GPT-4 scored 4. In a simulated bar exam, GPT-4 passed with a score around the top 10% of test takers, while GPT-3.5 – the most advanced version of the GPT-3 series – was at the bottom 10%.
Moreover, GPT-4 is… a true polyglot. While GPT’s English proficiency was already high in the GPT-3 and GPT-3.5 versions (with shot accuracy at 70.1%), its accuracy in the newest version increased to over 85%. Actually, it speaks 25 languages better than its ancestor spoke English – including Mandarin, Polish, and Swahili. That is pretty impressive, considering that most existing ML benchmarks are written in English.
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Token limits in GPT-3 vs. GPT-4
Context length is a parameter used to describe how many tokens can be used in a single API request. The original GPT-3 models released in 2020 set the max request value at 2,049 tokens. In the GPT-3.5, this limit was increased to 4,096 tokens (which is ~3 pages of single-lined English text). GPT-4 comes in two variants. One of them (GPT-4-8K) has a context length of 8,192 tokens, and the second one (GPT-4-32K) can process as much as 32,768 tokens, which is about 50 pages of text.
That being said, we can think about all the new use cases for GPT-4. With their ability to process 50 pages of text, it will be possible to use the new OpenAI models to create longer pieces of text, analyze and summarize larger documents or reports, or handle conversations without losing context. As presented by Greg Brockman in the interview for Techcrunch:
Previously, the model didn’t have any knowledge of who you are, what you’re interested in and so on. Having that kind of history [with the larger context window] is definitely going to make it more able … It’ll turbocharge what people can do.
But that’s not the end because apart from processing text inputs, GPT-4 can interpret other input types as well.
Input types in GPT-4 and GPT-3
Although the GPT-3 and GPT-3.5 models could only accept one form of input (text or code), the GPT-4 can receive photos as well. It produces text outputs from inputs that include both text and graphics.
The GPT-4 model may create captions, identify visible elements, or analyse the image depending on what you ask it to do. Among the examples included in the GPT-4 study material are models evaluating graphs, explaining memes, and even summarising articles using text and graphics. GPT-4's visual comprehension abilities are astounding, to say the least. Just look at it!
The capacity to interpret photos, in conjunction with the increased token limitations, offers up new applications for GPT-4, ranging from academic research to personal training or shopping assistance. Don't get too thrilled, though, because it may take some time until you can utilise this new GPT-4 talent. According to the OpenAI website, picture inputs are currently in the research phase and are not publicly available.
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Defining the context of GPT-4 vs. GPT-3 conversation
Another significant distinction between GPT-3 and GPT-4 is how we define the model's tone, style, and conduct.
With the most recent version of GPT, it is able to offer instructions to the model on an API level by inserting so-called "system" messages (within the boundaries described in the OpenAI Use policy). These instructions establish the tone of the communications and specify how the model should act (for example, "you never give the learner the answer but always attempt to ask exactly the perfect question to enable them learn to think for themselves"). Furthermore, they set boundaries for GPT-4's interactions, acting as "guardrails" to prevent GPT-4 from modifying its behaviour at the user's request, as seen in the following example:
Notwithstanding the user's demands, the GPT-4 remains within its duty, as defined in the system message.
We may already see a comparable model's capability in the previously launched GPT-3.5-Turbo. We may receive a different result by describing the model's role in a system prompt. Consider how the message changes based on who the GPT model is impersonating:
It was not able to give the model with the system message until March 2023, when the GPT-3.5-Turbo was launched. The context information had to be provided inside the prompt and was subject to alter throughout the dialogue.
The capabilities of the new GPT-4 allows it to be more consistent in its behaviour and more adaptable to external standards (e.g., your brand communication guidelines).
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Cost of using GPT-4 vs. GPT-3
Of course, there is a cost to all of this. While GPT-3 models range in price from $0.0004 to $0.02 per 1K tokens, and the newest GPT-3.5-Turbo model is 10 times less ($0.002 per 1K tokens) than the most powerful GPT davinci model, the cost of utilising GPT-4 is clear: if you want to utilise the most advanced models, you will have to pay more.
The GPT-4 will cost $0.03 for 1K prompt tokens and $0.06 per 1K completion tokens with an 8K context window. In contrast, the GPT-4 with a 32K context window will cost $0.06 for 1K prompt tokens and $0.12 per 1K completion tokens.
If processing 100k requests with an average duration of 1500 prompt tokens and 500 completion tokens costed $4,000 with text-davinci-003 and $400 with gpt-3.5-turbo, it would cost $7,500 with GPT-4 and $15,000 with the 32K context window.
It is not only more costly, but also more difficult to compute. This is due to the fact that the cost of prompt (input) tokens differs from the cost of completion (output) tokens. If you recall our GPT-3 pricing experiment, you will recall that calculating token consumption is challenging due to the poor connection between input and output length. The cost of employing GPT-4 models will become increasingly less predictable as the cost of the output (completion) tokens rises.
Fine-tuning of the OpenAI models
Remember how we defined the context in the GPT-4 and GPT-3.5-Turbo system messages? Fine-tuning is a workaround approach for defining the model's tone, style, and behaviour and tailoring GPT models to a given application.
You train the model on much more samples than the prompt allows for in order to fine-tune it. You don't need to offer examples in the prompt once a model has been refined. This saves money (every 1,000 tokens counts!) and allows for lower-latency queries. Doesn't it sound fantastic? Unfortunately, the only OpenAI models that can presently be fine-tuned are the original GPT-3 base models (davinci, curie, ada, and babbage).
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Errors and limitations
As several reports about GPT-4 surfaced (for example, the amount of parameters it employs), OpenAI's CEO stated that
The GPT-4 rumour mill is an absurdity. I'm not sure where it all comes from. They are asking to be let down, and they will be. (…) We don't have a genuine AGI, which is sort of expected of us.
While it's difficult to call GPT-4 unsatisfactory given its inventiveness and tremendous powers, it's crucial to understand its limits. Nevertheless, as we can see from the product research paperwork, they didn't modify anything from prior iterations of the model.
GPT-4, like its predecessors, is unaware of events that transpired after September 2021. Also, no matter how intelligent ChatGPT appears to be, it is not completely dependable - even when powered by GPT-4. Despite claims that it considerably reduces hallucinations compared to earlier models (scoring 40% higher than GPT-3.5 in internal assessments), it still "hallucinates" facts and makes logical mistakes. It can still provide damaging advise (though it is far more likely to refuse to respond), broken code, or false information, and as a result, it should not be employed in areas with significant mistake costs.
GPT-3 vs. GPT-4 – Key takeaways
GPT-4, OpenAI's most sophisticated system, outperforms prior versions of the models in practically every comparison. It is more imaginative and coherent than GPT-3. It can handle lengthier bits of text as well as graphics. It's more accurate and less prone to fabricate "facts" up. Its skills open up a slew of new possibilities for creative AI.
Does this imply that GPT-4 will supersede GPT-3 and GPT-3.5? Very likely not. Although GPT is more powerful than earlier OpenAI models, it is also significantly more expensive to operate. In many circumstances, when you don't require a model to handle multi-page documents or "remember" extended talks, GPT-3 and GPT-3.5 will suffice.
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