Key takeaways

• Gemini 2.5 Pro is a state-of-the-art, proprietary, Transformer-based AI model by Google, designed for advanced reasoning, coding, mathematics, and scientific tasks, emphasizing complex problem-solving through 'thinking' capabilities and nuanced context handling. It supports multimodal input (text+image-to-text) and has a context length of 1,048,576 tokens. Its primary purpose includes tackling problems that require advanced logical deduction and deep contextual understanding across various domains.
• The model demonstrates strong mathematical reasoning in algebra, geometry, number theory, and modular arithmetic, often excelling in systematic equation manipulation. It achieved 95.678% accuracy on the Grade School Math 8K dataset, indicating a strong ability in multi-step arithmetic problems and extracting numerical information from word problems. It also showed strong performance in many AIME 2025 sub-areas.
• Key limitations include significant struggles with complex algorithmic code synthesis (as noted in its description, with 1.61% accuracy on LiveCodeBench, though not directly provided in the evaluation data). It also exhibits weaknesses in complex combinatorial analysis, geometric problems requiring precise path optimization, and multi-variable probabilistic reasoning, often showing subtle calculation errors or misinterpreting conditional probabilities.
• The model excels at problems requiring systematic manipulation of equations, function analysis, and large number modular arithmetic, as evidenced by its strong performance in various AIME 2025 sub-areas like algebraic relationships, geometric theorems, arithmetic progression, prime factorization, and recurrence relations. It consistently arrives at exact answers for complex mathematical problems.
• Weaknesses were evident in AIME 2025 Part I and II for problems involving complex combinatorial analysis, such as grid coloring or counting arrangements with specific restrictions, where it sometimes failed due to meticulous casework challenges or minor arithmetic errors. Probabilistic reasoning in multi-variable geometric contexts and geometric optimization problems also posed challenges, often leading to misinterpretations of conditions or incorrect formulas.

Technical specifications

Context length

1M tokens

Max output

—

Architecture

Transformer

Modalities

text+image->text

Parameters

~175B

Open weights

No

Licence

Proprietary

Regions

USA

Vendor

Google

Provider model key

google/gemini-2.5-pro