Gemini 2.5 Deep Think - Google's AI reasoning model

What is Gemini 2.5 Deep Think

Gemini 2.5 Deep Think is an AI model launched by Google, designed specifically to solve complex tasks. It is a variant of the model that won a gold medal at the 2025 International Mathematical Olympiad (IMO). Through Parallel Thinking technology and reinforcement learning, it can simultaneously explore multiple solutions, verify and optimize them against each other, and ultimately arrive at the best answer. Deep Think is particularly good at handling complex math problems, algorithm design, scientific research reasoning, and creative development tasks.

In terms of performance, Deep Think performs excellently in multiple benchmark tests. For example, it achieved the highest score of 34.8% on the HLE test, was close to a perfect score on AIME 2025, and also achieved a high score of 87.6% on LiveCodeBench V6. It can generate more detailed and more creative outputs, especially excelling in complex tasks. Deep Think is only available to Google AI Ultra subscribers, with a monthly fee of $249.99 (about 1,800 RMB), and there is a fixed daily usage quota.

Main features of Gemini 2.5 Deep Think

• Parallel Thinking: Through Parallel Thinking technology, Deep Think can simultaneously generate multiple ideas and evaluate them. It explores multiple solutions at the same time, verifies and optimizes them against each other, and ultimately arrives at the best answer. This is similar to how humans think from multiple angles when solving complex problems.
• Reinforcement Learning: Through new reinforcement learning techniques, Deep Think can continuously optimize its reasoning paths over time and become better at solving problems.
• Mathematics and Algorithms: Deep Think performs excellently in mathematics and algorithm design. It can solve complex math problems, for example winning a gold medal at the 2025 International Mathematical Olympiad (IMO) and scoring close to perfect on AIME 2025.
• Scientific Research Reasoning: Deep Think can help researchers propose and verify mathematical conjectures, reason about complex scientific literature, and accelerate the process of scientific discovery.
• Iterative Development: Deep Think performs excellently in tasks that require building complex things step by step. For example, in web design, game scene modeling, and product prototype optimization, it can simultaneously improve the project's aesthetics and functionality.
• Voxel Art: When generating complex creative designs (such as voxel art), Deep Think can produce richer and more detailed outputs. Compared with other versions of Gemini models, the detail and aesthetics are significantly improved.
• Highly Difficult Programming Problems: Deep Think performs excellently when handling programming problems that require precise problem formulation, trade-offs, and time complexity. It can help programmers break down problems, model algorithms, and gradually approach the optimal solution.
• Code Optimization: In the LiveCodeBench V6 test, Deep Think achieved a high score of 87.6%, demonstrating its strong capabilities in code optimization and algorithm design.
• Content Safety and Objectivity: Deep Think's content safety and objectivity have improved compared with Gemini 2.5 Pro, and it can better handle sensitive and complex content.
• Refusing Benign Requests: Although the tendency to refuse benign requests has increased, this ensures the model's rigor and safety when handling complex tasks.

Technical principles of Gemini 2.5 Deep Think

• Multithreaded Reasoning: Deep Think can simultaneously generate and consider multiple lines of thought, revise or merge different ideas over time, and ultimately arrive at the best answer.
• Extended Thinking Time: By extending reasoning time, the model has more opportunities to explore different hypotheses and find more creative solutions for complex problems.
• Optimized Reasoning Paths: Reinforcement learning techniques enable Deep Think to continuously optimize its reasoning paths over time, becoming a better and more intuitive problem solver.
• Dynamic Adjustment: Users can set a thinking budget to balance performance and cost.
• Sparse Mixture-of-Experts (MoE) Architecture: Deep Think is based on a sparse mixture-of-experts architecture, which allows the model to activate a subset of the model parameters for each input token. Specific features include:
  • Dynamic Routing: The model learns to dynamically route tokens to subsets of parameters (experts), decoupling total model capacity from the computation and serving cost per token.
  • Efficient Computation: This architecture enables the model to efficiently process large-scale inputs while maintaining high performance.

Gemini 2.5 Deep Think Project Address

• Project Official Website：https://blog.google/products/gemini/gemini-2-5-deep-think/
• Technical Paper：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 Comparison

Application Scenarios of Gemini 2.5 Deep Think

• Mathematics and Algorithms: Reaches gold medal level in the International Mathematical Olympiad (IMO) and nearly a perfect score in AIME 2025.
• Scientific Research Reasoning: Helps researchers propose and verify mathematical conjectures and reason about complex scientific literature.
• Creativity and Design: Performs excellently in tasks such as web design and game scene modeling, and can generate richer and more detailed outputs.
• Designers: Generate complex creative designs and optimize web pages and game scenes.
• Students and Educators: Assist in solving complex mathematics and science problems.