Google DeepMind’s chief scientist, Jeff Dean, says that the model receives extra computing power, writing on X, “we see promising results after we increase inference time computation!” The model works by pausing to contemplate multiple related prompts before providing what it determines to be essentially the most accurate answer.
Since OpenAI’s jump into the “reasoning” field in September with o1-preview and o1-mini, several firms have been rushing to realize feature parity with their very own models. For instance, DeepSeek launched DeepSeek-R1 in early November, while Alibaba’s Qwen team released its own “reasoning” model, QwQ earlier this month.
While some claim that reasoning models can assist solve complex mathematical or academic problems, these models may not be for everyone. While they perform well on some benchmarks, questions remain about their actual usefulness and accuracy. Also, the high computing costs needed to run reasoning models have created some rumblings about their long-term viability. That top cost is why OpenAI’s ChatGPT Pro costs $200 a month, for instance.
Still, it appears Google is serious about pursuing this particular AI technique. Logan Kilpatrick, a Google worker in its AI Studio, called it “step one in our reasoning journey” in a post on X.
