A Google Gemini model now has a "dial" to adjust how much it reasons

"We've been really pushing on'thinking,'" says Jack Rae, a principal research scientist at DeepMind. Such models, which are built to work through problems logically and spend more time arriving at an answer, rose to prominence earlier this year with the launch of the DeepSeek R1 model. They're attractive to AI companies because they can make an existing model better by training it to approach a problem pragmatically. That way, the companies can avoid having to build a new model from scratch. When the AI model dedicates more time (and energy) to a query, it costs more to run.