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Google has officially launched its next-generation AI model, Gemini 2.0 Pro Experimental, in a low-profile update rather than a major announcement. The new model was revealed through a changelog update in the Gemini chatbot app. However, some users reported that mentions of the model were later removed from the changelog, with Google attributing it to an "out-of-date release note" published in error.
Gemini 2.0 Pro Experimental Now Available
The new model succeeds the Gemini 1.5 Pro version, which was launched in February. It is available to Gemini Advanced users as part of Google’s AI Premium plan and through Gemini for Google Workspace add-ons. According to Google, Gemini 2.0 Pro Experimental is designed to offer "better factuality" and "stronger performance" for coding and mathematics-related tasks.
Google describes the model as particularly useful for advanced coding challenges and complex mathematical problem-solving, including developing statistical models and quantum algorithms. However, it is currently in an "early preview" stage, meaning it may exhibit unexpected behavior and is not compatible with some existing Gemini app features.
Gemini 2.0 Flash Model Now in the Gemini App
Alongside the release of Gemini 2.0 Pro Experimental, Google has integrated the Gemini 2.0 Flash model into the Gemini app. Originally announced in December, the Flash model is now the default for all users and will remain so for the foreseeable future.
?? Gemini is now powered by 2.0 Flash, our latest model that is designed for the agentic era, delivering fast responses and stronger performance across a number of key benchmarks for everyday help with tasks like brainstorming, learning, or writing. Rolling out to all users… pic.twitter.com/9ACTY0nPC4
— Google Gemini App (@GeminiApp) January 30, 2025
Google emphasized its commitment to rapid iteration and innovation, stating that feedback from early users of Gemini 2.0 Pro Experimental will help refine future models. The company aims to gradually expand the availability of its AI advancements based on user input and experimental learning.

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