Gemini 2.5 Deep Think: Parallel AI for Creative Problem-Solving
Google has unveiled Gemini 2.5 Deep Think, a sophisticated artificial intelligence model designed to tackle complex creative problems through an innovative approach that leverages parallel thinking and extended computational time. Available as part of the Google AI Ultra subscription, Deep Think is specifically engineered for challenges demanding creativity, strategic planning, and a meticulous, step-by-step methodology. This includes intricate tasks such as iterative design and development, groundbreaking scientific and mathematical discovery, and advanced algorithm creation.
The currently available iteration of Deep Think represents a significant optimization and refinement of the model that achieved a gold-medal performance at the 2025 International Mathematical Olympiad (IMO). During that highly competitive event, an advanced variant of Gemini Deep Think flawlessly solved five out of six challenging problems, accumulating a remarkable 35 total points—a score indicative of gold-medal excellence. While that competition-specific version could dedicate hours to reasoning through complex problems, the new public release trades a degree of reasoning depth for enhanced speed, rendering it more practical for everyday applications.
This advancement marks a substantial leap for Google’s AI efforts compared to their performance at the 2024 IMO. In the previous year, models like AlphaProof and AlphaGeometry necessitated human experts to first translate problems from natural language into specialized domain-specific languages, and even then, took up to three days to generate solutions. Deep Think, by contrast, streamlines this process considerably.
At its core, Gemini Deep Think operates by simultaneously generating multiple potential solutions to a problem. Throughout its reasoning process, it continuously revises and combines these alternatives, iteratively converging on the most optimal answer. This parallel exploration and refinement demand a significantly extended reasoning time window, making the model less suitable for instantaneous, interactive applications such as real-time chat. Google acknowledges that users may occasionally encounter slower response times or timeout issues due to this intensive computational requirement.
Further distinguishing Deep Think from other models within the Gemini family is its unique training methodology. It incorporates novel reinforcement learning techniques that actively encourage the model to utilize these prolonged reasoning paths, allowing it to test and validate numerous hypotheses concurrently. Moreover, the model has been trained with access to a meticulously curated corpus of high-quality solutions to complex mathematical problems, further enhancing its problem-solving capabilities.
Google asserts that Gemini 2.5 Deep Think sets new benchmarks, achieving state-of-the-art performance on several critical evaluations, including LiveCodeBench V6 and Humanity’s Last Exam. However, early adopters have noted a significant practical limitation: the restricted number of queries available even to paying users. Initially capped at five per day, this limit was later doubled to ten. Some observers interpret this restriction as a strong indication of the substantial computational expense involved in running the model, potentially equivalent to operating a large cluster of Gemini Pro models in parallel.
Like its counterparts in the Gemini series, Deep Think is built upon a sparse mixture-of-experts (MoE) architecture. It also boasts native multimodal support, capable of processing text, vision, and audio inputs seamlessly. The model features an impressive 1-million-token input context window and a 192,000-token output window, signifying its capacity to handle vast amounts of information. Beyond the optimized version offered to AI Ultra subscribers, Google has also made the competition variant available to the broader research community, fostering further innovation and study in the field.