Gemini 2.5 Deep Think - Google's AI inference model

What is Gemini 2.5 Deep Think?

Gemini 2.5 Deep Think is an AI model developed by Google, designed to solve complex tasks. It's a variant of the model that won a gold medal at the 2025 International Mathematical Olympiad (IMO). Through parallel thinking and reinforcement learning, it can simultaneously explore multiple solutions, cross-validate and optimize them, ultimately arriving at the best answer. Deep Think excels at handling complex mathematical problems, algorithm design, scientific reasoning, and creative development tasks.

In terms of performance, Deep Think excels in multiple benchmark tests, achieving a top score of 34.8% in the HLE test, near-perfect scores in AIME 2025, and a high score of 87.6% in LiveCodeBench V6. It generates more detailed and creative output, performing particularly well in complex tasks. Deep Think is only available to Google AI Ultra subscribers, costing $249.99 per month (approximately 1800 RMB), with a fixed daily usage limit.

Main functions of Gemini 2.5 Deep Think

• Parallel ThinkingDeep Think utilizes parallel thinking technology to generate and evaluate multiple ideas simultaneously. It explores various solutions, verifies and optimizes them, and ultimately arrives at the best answer. This is similar to the multi-faceted thinking humans use when solving complex problems.
• reinforcement learningThrough new reinforcement learning techniques, Deep Think can continuously optimize its reasoning path over time, becoming better at solving problems.
• Mathematics and AlgorithmsDeep Think excels in mathematics and algorithm design. It can solve complex mathematical problems, such as winning a gold medal at the 2025 International Mathematical Olympiad (IMO) and achieving near-perfect scores in AIME 2025.
• Scientific reasoningDeep Think can help researchers propose and verify mathematical conjectures, reason about complex scientific literature, and accelerate the process of scientific discovery.
• Iterative developmentDeep Think excels at tasks that require building complex things step by step. For example, in web design, game scene modeling, and product prototype optimization, it can improve both the aesthetics and functionality of a project.
• Voxel ArtWhen generating complex creative designs (such as voxel art), Deep Think can produce richer and more detailed outputs, with significantly improved detail and aesthetics compared to other versions of Gemini models.
• High-difficulty programming problemsDeep Think excels at handling programming problems that require precise problem formulation, trade-offs, and time complexity. It helps programmers break down problems, model algorithms, and gradually approach the optimal solution.
• Code optimizationIn the LiveCodeBench V6 test, Deep Think achieved a high score of 87.6%, demonstrating its strong capabilities in code optimization and algorithm design.
• Content security and objectivityDeep Think offers improved content security and objectivity compared to Gemini 2.5 Pro, enabling it to better handle sensitive and complex content.
• Reject benign requestsAlthough the tendency to reject benign requests has increased, it ensures the rigor and safety of the model when dealing with complex tasks.

The technical principles of Gemini 2.5 Deep Think

• Multi-threaded inferenceDeep Think can generate and consider multiple ideas simultaneously, revising or integrating different ideas over time to arrive at the best answer.
• Extend thinking timeBy extending the inference time, the model has more opportunities to explore different hypotheses and find more creative solutions to complex problems.
• Optimize reasoning pathReinforcement learning technology enables Deep Think to continuously optimize its reasoning path over time, becoming a better and more intuitive problem solver.
• Dynamic adjustmentUsers can set a budget to balance performance and cost.
• Sparse Hybrid Expert (MoE) ArchitectureDeep Think is based on a sparse hybrid expert architecture, allowing the model to activate a subset of model parameters for each input token. Specific features include:
  • Dynamic routingThe model learns to dynamically route tokens to a subset of parameters (experts), decoupling the total model capacity from the computational and service costs per token.
  • High-efficiency computingThis architecture enables the model to efficiently handle large-scale inputs while maintaining high performance.

The project address for Gemini 2.5 Deep Think.

• Project official website: https://blog.google/products/gemini/gemini-2-5-deep-think/
• Technical Papers: https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-2-5-Deep-Think-Model-Card.pdf

Gemini 2.5 Deep Think vs. Gemini 2.5 Pro

Application Scenarios of Gemini 2.5 Deep Think

• Mathematics and AlgorithmsAchieve a gold medal in the International Mathematical Olympiad (IMO) and a near-perfect score in AIME 2025.
• Scientific reasoningIt helps researchers propose and verify mathematical conjectures and reason about complex scientific literature.
• Creativity and DesignIt performs exceptionally well in tasks such as web design and game scene modeling, generating richer and more detailed output.
• DesignerGenerate complex creative designs and optimize web pages and game scenes.
• Students and educatorsIt helps solve complex mathematical and scientific problems.