Gemini's 'Thinking' models are trained with Reinforcement Learning to utilize additional compute at inference time for more accurate answers[1]. These models can spend tens of thousands of forward passes during a 'thinking' stage before responding to a query[1]. This is integrated with other Gemini capabilities, such as multimodal inputs and long context, where the model decides how long to think before answering[1].
Users can also set a Thinking budget, which constrains the model to respond within a desired number of tokens, allowing for a tradeoff between performance and cost[1]. The Gemini 2.5 Thinking models are the most well-rounded reasoning models to date[1].
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