Google’s Gemini Deep Think model achieved gold medal-level performance at the International Mathematical Olympiad by using parallel chains of thought to solve complex problems, though it requires lengthy processing times of over 10 minutes per solution. While demonstrating impressive reasoning and creative capabilities, such as generating 3D voxel art and coding prototypes, its slow speed limits practicality, suggesting it is best used alongside faster AI models for iterative development.
The video discusses Google’s Gemini Deep Think model, which recently achieved gold medal standard performance at the International Mathematical Olympiad (IMO), a prestigious competition for high school students involving challenging math problems. The IMO consists of six problems, each worth seven points, with a maximum score of 42. Last year, Google’s DeepMind system scored a silver medal equivalent by earning 28 points, but it took significantly longer than human competitors to solve the problems. This year, instead of using specialized mathematical languages or structured proof systems like AlphaProof or AlphaGeometry, Google employed an advanced version of Gemini with Deep Think, a model designed to generate multiple chains of thought in parallel to improve reasoning and problem-solving.
Deep Think was first announced at Google IO and has been in testing for over a month. The model excels in mathematics, coding, and logical reasoning tasks but requires substantial processing time, often taking 10 minutes or more to generate answers. This is due to its parallel thinking approach, where it explores many possible solution paths simultaneously before selecting the best one. The video demonstrates this by running an IMO problem through Deep Think, which took over 16 minutes to produce a correct answer. While the model’s intelligence is impressive, its long response times make it less practical for everyday use, though it is well-suited for complex problems requiring deep reasoning.
The video also compares Google’s achievement with OpenAI’s recent announcement of a similar gold medal-level performance at the IMO, where OpenAI released proofs for five of the questions they solved. Both companies withheld solutions for one problem, possibly due to the complexity or quality of the answers. The presenter notes that some believe DeepMind’s solutions were more human-like in style. The availability of these proofs allows for some comparison, though the presenter refrains from judging which is superior. The focus then shifts back to exploring Gemini Deep Think’s capabilities through various examples beyond math, including generating 3D voxel art and creating a simple Angry Birds-style game.
In the creative tasks, Gemini Deep Think shows promise, successfully generating colorful 3D voxel art using 3JS and demonstrating an understanding of spatial concepts. However, when tasked with coding a game, the model faced challenges such as limited environment support and incomplete physics simulation. The presenter highlights that while Deep Think can produce initial architectures or prototypes, its slow response times and occasional limitations suggest it may be better suited as a complementary tool alongside faster models like Gemini 2.5 Pro for iterative development. The video emphasizes the trade-off between intelligence, speed, and practicality when using such advanced AI systems.
In conclusion, Gemini Deep Think represents a significant advancement in AI reasoning and problem-solving, particularly for complex mathematical and logical tasks. It is currently accessible through the Gemini app for users with an Ultra subscription and will soon be available via API on platforms like AI Studio and Google Cloud. Despite its impressive capabilities, the model’s lengthy processing times pose challenges for widespread adoption. The video suggests that future AI development will need to balance intelligence with speed and cost to create more practical and efficient tools. The presenter encourages viewers to like and subscribe for more updates on AI advancements.