Gemini 2.0 Flash Vision
Gemini 2.0 Flash delivers next-gen features and improved capabilities, including superior speed, built-in tool use, multimodal generation, and a 1M token context window.
Google Multimodal vision model with 1M token context
Gemini 2.0 Flash Vision is a multimodal language model developed by Google, designed to process and reason over text, images, and other input types within a single context window of up to 1,048,576 tokens. It is part of the Gemini 2.0 Flash family, which emphasizes speed and efficiency alongside broad capability coverage including built-in tool use and multimodal generation. The model's training data has a cutoff of June 2024.
Gemini 2.0 Flash Vision is well-suited for tasks that require understanding visual content alongside large volumes of text, such as document analysis, image-based question answering, and long-context reasoning. Its large context window makes it practical for workflows involving lengthy documents or multi-turn conversations that incorporate both images and text. The model is accessible through Google's Vertex AI platform and is intended for developers building applications that need fast, multimodal processing at scale.
What Gemini 2.0 Flash Vision supports
Image Understanding
Analyzes and reasons over image inputs alongside text, enabling tasks like visual question answering and image-based document analysis.
Long Context Window
Supports up to 1,048,576 tokens in a single context, allowing processing of lengthy documents, multi-image inputs, or extended conversations.
Multimodal Generation
Generates responses that draw on multiple input modalities, combining text and visual understanding in a single inference pass.
Built-in Tool Use
Supports native tool-calling capabilities, enabling the model to invoke external functions or APIs as part of its response generation.
Fast Inference
Optimized for low-latency responses within the Gemini 2.0 Flash family, making it suitable for real-time or high-throughput applications.
Structured Output
Can return responses in structured formats, supporting downstream data extraction and integration workflows.
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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 | 77.9% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 62.3% |
| MATH-500 | Undergraduate and competition-level math problems | 93.0% |
| AIME 2024 | American math olympiad problems | 33.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 33.4% |
| HLE | Questions that challenge frontier models across many domains | 5.3% |
| SciCode | Scientific research coding and numerical methods | 33.3% |
Common questions about Gemini 2.0 Flash Vision
What is the context window size for Gemini 2.0 Flash Vision?
Gemini 2.0 Flash Vision supports a context window of 1,048,576 tokens, allowing it to process very large documents or extended multi-turn conversations in a single request.
What is the training data cutoff for this model?
The model's training data has a cutoff of June 2024, meaning it does not have knowledge of events or information published after that date.
What input types does Gemini 2.0 Flash Vision accept?
The model is classified as a Vision type and accepts both text and image inputs, enabling multimodal tasks that combine visual and textual content.
How is Gemini 2.0 Flash Vision accessed?
The model is available through Google's Vertex AI platform. Documentation for deployment and API usage is provided via the Vertex AI generative AI docs.
Does Gemini 2.0 Flash Vision support tool use?
Yes, Gemini 2.0 Flash Vision includes built-in tool use capabilities, allowing it to call external functions or APIs as part of generating a response.
Parameters & options
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