OpenAI approves more than new $200 monthly subscription, ChatGPT Pro, that includes its o1 reasoning model

Avijit SahDecember 6, 2024

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OpenAI’s latest news is it has launched a very new subscription plan for its AI-powered chatbot platform. That is very expensive. The most important query is if this expensive package covers all the requirements of the audience. This launch is confirmed by the leaked news of the Open AI. Open AI announced the Chat GPT pro with a $200-per-month subscription package. Through getting this package users will get unlimited access to all the Open AI models that include the full versions of its o1 “reasoning” model.

Jason Wei, a member of OpenAI’s technical staff said during a live-streaming press conference on Thursday.

“We think the audience for ChatGPT Pro will be the power users of ChatGPT — those who are already pushing the models to the limits of their capabilities on tasks like math, programming, and writing,”.

Online the other AI power models are attempting to check their own work as they do. This will help them to avoid the pitfalls which normally trip up the models. The downside is they often take longer to arrive in the situation. The O1 reasons come through the functions, planning, and performing the action sequence that is going to help the model and knock out the answers.

OpenAI emitted

A preview option of o1 in September. That new version is generally speaking in a more performing and prominent way. If you compare the news with the previews users can expect

“more concise in its thinking” to improve response times. According to OpenAI’s internal testing, o1 reduces “major errors” on “difficult real-world questions”

Also, o1 can reason about the uploaded image. This is also not possible during the previews that have been trained to be ‘more concise in its thinking’. This will improve the response time and according to the Open AI and its internet testing,o1 reduces the “major errors” on the basis of the “difficult real-world questions” through 34% comparison to the preview versions on a multiple number of benchmarks. One of these benchmarks is based on the MLE Bench that measures how well AI agents perform at machine learning engineering.