Gemini 2.5 Pro is Google's most advanced AI model, engineered for deep reasoning and thoughtful response generation. It outperforms on key benchmarks, demonstrating exceptional logic and coding proficiency. Optimized for building dynamic web applications, autonomous code systems, and code adaptation, it delivers high-level performance. With built-in multimodal capabilities and an extended context window, the model efficiently processes large datasets and integrates diverse information sources to tackle complex challenges.
Web Site AI Model Web Page | |
Provider The entity that provides this model. | |
Chat Input a message to start chatting | - |
Release Date When the model was first released. | 1 year ago Mar 25, 2025 |
Modalities Types of data this model can process | text images voice video |
API Providers The providers that offer this model. (This is not an exhaustive list.) | Google AI Studio, Vertex AI, Gemini app |
Knowledge Cut-off Date When the model's knowledge was last updated. | - |
Open Source Whether the model's code is available for public use. | No |
Pricing Input Cost for processing tokens in your prompts | Not available |
Pricing Output Cost for tokens generated by the model | Not available |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | Not available |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | Not available |
MMMU Massive Multitask Multimodal Understanding - Tests understanding across text, images, audio, and video | 81.7% Source |
HellaSwag A challenging sentence completion benchmark | Not available |
HumanEval Evaluates code generation and problem-solving capabilities | Not available |
MATH Tests mathematical problem-solving abilities across various difficulty levels | Not available |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 84.0% Diamond Science Source |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | Not available |
SimpleQA Assessing the accuracy of simple questions | 52.9% |
AIME 2024 | 92.0% |
AIME 2025 | 86.7% |
Aider Polyglot Multilingual programming benchmark. | 74.0% / 68.6% |
LiveCodeBench v5 Benchmark for real-time programming | 70.4% |
Global MMLU (Lite) A simplified version of the benchmark for assessing the universality of models at the global level. | 89.8% |
MathVista Evaluates the mathematical reasoning abilities of AI models within visual contexts | - |
Mobile Application | |
VideoGameBench | |
| Total score | 0.48% |
| Doom II | 0% |
| Dream DX | 4.8% |
| Awakening DX | 0% |
| Civilization I | 0% |
| Pokemon Crystal | 0% |
| The Need for Speed | 0% |
| The Incredible Machine | 0% |
| Secret Game 1 | 0% |
| Secret Game 2 | 0% |
| Secret Game 3 | 0% |
MathArena | |
| Avg. Score | 81% |
| AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | 87% |
| HMMT February 2025 A test based on problems from the Harvard-MIT Mathematics Tournament, February 2025, designed to assess the mathematical skills of models. | 82% |
| BRUMO 2025 | 90% |
| SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | 85% |
| CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | 58% |
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Comments (1)
Mazen
11 August 2025Good program