Gemini 2.5 Pro Vision
Gemini 2.5 Pro is our most advanced reasoning Gemini model, capable of solving complex problems.
Google Advanced reasoning with multimodal vision support
Gemini 2.5 Pro Vision is a multimodal AI model developed by Google DeepMind, designed to reason through complex problems by analyzing text, images, audio, video, and code. It operates as a "thinking model," meaning it works through logical steps before producing a response rather than generating output directly. The model supports a context window of 1,048,576 tokens, enabling it to process large documents, codebases, and extended conversations in a single request.
The model is particularly suited for tasks that require combining visual understanding with structured reasoning, such as interpreting diagrams, analyzing image-based data, and generating code from visual inputs. It has demonstrated strong benchmark performance in math, science, and software engineering tasks, including a 63.8% score on the SWE-Bench Verified evaluation. Gemini 2.5 Pro Vision is available through Google AI Studio and via the Gemini API, making it accessible for developers building applications that require both vision and reasoning capabilities.
What Gemini 2.5 Pro Vision supports
Extended Context Window
Processes up to 1,048,576 tokens in a single request, allowing entire codebases, long documents, or extended conversations to be handled without truncation.
Multimodal Input
Accepts text, images, audio, video, and code as inputs within the same request, enabling cross-modal analysis and generation.
Structured Reasoning
Uses a chain-of-thought approach to work through multi-step problems before producing a final answer, improving accuracy on complex tasks.
Code Generation
Generates and analyzes code across multiple languages, achieving 63.8% on the SWE-Bench Verified benchmark for software engineering tasks.
Math and Science Tasks
Applies logical and quantitative reasoning to solve problems in mathematics and science, with benchmark results reflecting strong performance in these domains.
Visual Understanding
Interprets and reasons over images and video frames, supporting use cases like diagram analysis, chart reading, and image-based question answering.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 86.2% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 84.4% |
| MATH-500 | Undergraduate and competition-level math problems | 96.7% |
| AIME 2024 | American math olympiad problems | 88.7% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 80.1% |
| HLE | Questions that challenge frontier models across many domains | 21.1% |
| SciCode | Scientific research coding and numerical methods | 42.8% |
Common questions about Gemini 2.5 Pro Vision
What is the context window size for Gemini 2.5 Pro Vision?
Gemini 2.5 Pro Vision supports a context window of 1,048,576 tokens, which allows it to process large volumes of text, images, and other inputs in a single request.
What is the training data cutoff for this model?
According to the model metadata, the training date is listed as June 2025.
What input types does Gemini 2.5 Pro Vision support?
The model supports multimodal inputs including text, images, audio, video, and code, making it suitable for tasks that combine visual and language understanding.
Where can I access Gemini 2.5 Pro Vision?
The model is available through Google AI Studio, the Gemini API, and Google Cloud Vertex AI, as well as through MindStudio without requiring separate API key management.
Is Gemini 2.5 Pro Vision suitable for coding tasks?
Yes. The model scored 63.8% on the SWE-Bench Verified evaluation, which measures performance on real-world software engineering tasks, and it supports code generation and analysis across multiple programming languages.
Parameters & options
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