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Google has introduced a new AI model, Gemini 2.0 Flash Thinking Experimental, designed for advanced reasoning, capable of tackling complex problems in various fields. Although it promises significant improvement, it is still experimental and requires time to produce optimal answers.

Key points:

• Gemini 2.0 Flash Thinking Experimental is Google’s new AI model, designed for complex reasoning in programming, mathematics, and physics.
• This system is designed to analyze problems in greater depth than traditional AI models.
• Despite its advances, the model requires significant computing power and longer response times.
• Other players in the AI ​​field are developing similar models, trying to compete with Google and OpenAI’s solutions.

Google recently released a new version of its AI model, called Gemini 2.0 Flash Thinking Experimental, which is billed as a significant step forward in the field of AI “reasoning.” Available on AI Studio, the company’s prototyping platform, this model was designed to tackle particularly complex problems in fields such as programming, mathematics and physics. Although still in an experimental development phase, Gemini 2.0 Flash Thinking Experimental stands out for its ability to reason about intricate questions, going beyond traditional AI models.

AI Studio product manager Logan Kilpatrick said that this version represents the company’s “first step in the reasoning journey,” while Jeff Dean, Google DeepMind’s chief scientist, explained that the model was trained to use thinking as a tool to improve the quality of its answers. An innovative aspect of Gemini 2.0 Flash Thinking Experimental is the way the model approaches the inference process: rather than immediately providing an answer, the system pauses to think through possible approaches, analyzing a series of related prompts and explaining its reasoning in real time. Only after exploring several possibilities does the model provide what it considers to be the most accurate answer.

However, this ability to “think” comes at the cost of processing time. While a traditional model can answer a question quickly, Gemini 2.0 Flash Thinking Experimental takes longer, often minutes, to reach a conclusion. While this is a trade-off for accuracy, it raises questions about operational efficiency, especially in scenarios where speed of response is crucial. Nevertheless, some experiments with the model have produced encouraging results, such as the answer to the presence of “R” in the word “strawberry,” although results may vary depending on the circumstances.

The launch of this model is part of an increasingly competitive landscape, with several players in the industry developing similar solutions. In fact, in early November, the company DeepSeek presented its reasoning model, DeepSeek-R1, while Alibaba unveiled an alternative that aims to challenge OpenAI models, such as the more famous "o1". Google, for its part, is also investing considerable resources in this type of research, with over 200 researchers dedicated to the development of advanced reasoning models.

The growing attention towards reasoning models is motivated by the need to improve the effectiveness of generative AI, overcoming the limitations of traditional techniques, which, despite being powerful, have seen a slowdown in improvements. However, the adoption of complex models such as Gemini 2.0 Flash Thinking Experimental brings with it challenges, including the high costs associated with the computing power required and the uncertainty regarding the ability of these models to maintain a sustainable pace of development in the long term.

Ultimately, while Gemini 2.0 Flash Thinking Experimental and its competitors are still far from perfect, these models represent a significant step in the evolution of AI, bringing with them potential and challenges that could shape the future of this field.