There are many users who publish their gpt-4 failures on reddit and medium, from collective mockery of their problem solving to formal explanations of their limited reasoning abilities.
2) new nomenclature
although its name is not the most exciting thing about the new openai llm, it is an intentionally significant change.
Openai o1 is the first model to emerge from the 'gpt' moniker, and this is because the company claims that it is the first phase of a new 'reasoning paradigm', while previous models were part of a 'pre-training paradigm'.
The new model spends time reasoning in philippines mobile phone number real time, rather than relying on its pre-training data.
Video game coding with openai o1
3) longer waiting time
reasoning in real time takes more time than referencing training data and generating a response. If you ask openai o1-preview a question compared to other models, you will wait significantly longer.
However, with the ability to outsource reasoning, it is a small price to pay. The speed of the o1 models is likely to improve as subsequent models in the series come to market.
4) identical contextual windows
although many speculated an increase in gpt-4 content windows to the next model, the current o1 series remains identical to the gpt-4o content window of 128,000.
Context windows represent the number of tokens (words or subwords) that a model can process at a time. A larger context window allows the model to absorb more information from the input text, resulting in greater accuracy in its response.
One of gpt-4's shortcomings has been its comparatively limited ability to process large amounts of text. For example, gpt-4 turbo and gpt-4o have a context window of 128,000 tokens. But google's gemini model has a context window of up to 1 million tokens.
Right now, if your only concern is a broad language model that can absorb large amounts of information, the openai llms might not be your best choice. If you are curious to know which llm chatbot is best for you, check out our article on the best llm chatbots.
Two blue circles, one 7.6 times larger than the other. They represent the context window sizes of gpt-4 turbo and gemini.
Visual comparison of gpt-4 128,000 token turbo context window vs google gemini 1 million token context window.
What training data does gpt-5 use?
If there has been any reckoning for openai in its rise to the top of the industry, it has been the series of demands over the full training of models.
Gpt are trained from huge data sets extracted from the internet, many of them protected by copyright. This unauthorized use of data has led to numerous complaints and legal actions: a lawsuit from the new york times, another from a series of us news agencies, and allegations that the model training process violates the general data protection regulation of the eu
a california judge has already dismissed one of openai's copyright lawsuits brought by a group of writers, including the famous sarah silverman and ta-nehisi coates. There is no indication yet that openai and company will be substantially slowed down by these lawsuits as they continue their testing.
