Image Understanding
Analyzes and reasons over image inputs alongside text, enabling tasks like visual question answering and image-based document analysis.
Google
Gemini 2.0 Flash Vision
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.
Feb 05, 2025
1,048,576 context
8,192 tokens output
Image Understanding
Long Context Window
Multimodal Generation
Built-in Tool Use
Fast Inference
Structured Output
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Google
Input Context Window
The number of tokens supported by the input context window.
1,048,576
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
8,192 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Feb 05, 2025
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
June 2024
API Providers
The providers that offer this model. This is not an exhaustive list.
Google, Vertex AI
Modalities
Types of data this model can process.
Text
Image
What is Gemini 2.0 Flash Vision
A fuller summary of positioning, capabilities, and source-specific details for Gemini 2.0 Flash Vision.
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.
Capabilities
What Gemini 2.0 Flash Vision supports
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.
Pricing for Gemini 2.0 Flash Vision
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.15
Per million tokens
Output tokens
N/A
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
2
maxResponseSize
8,192 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Google
Vertex AI
Configuration & Parameters
The configurable options currently documented for this model.
Temperature
NumberMax Response Tokens
NumberSupported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
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AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MATH-500
Undergraduate and competition-level math problems
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about Gemini 2.0 Flash Vision
Gemini 2.0 Flash Vision discussions are most active in r/GoogleGeminiAI. Top Reddit threads cluster around benchmark and model-comparison threads. The strongest match in this snapshot has 0 upvotes and 4 comments.
I've been experimenting with the new **Gemini 2.0 Flash API** to see if it can handle real-time OCR on difficult surfaces (like curved bottles or crinkled food wrappers).
I hooked it up to a simple Streamlit UI to test the latency.
**My Findings:**
* **Speed:** It is significantly faster than GPT-4o for this specific vision task (returns analysis in \~2 seconds).
* **Hallucinations:** It seems to be much better at reading small "legal text" on ingredient lists without making things up.
I built a little tool called "LabelLens" to stress-test it against Ayurvedic ingredient lists.
Has anyone else noticed better OCR performance with Flash 2.0 vs 1.5 Pro? The speed difference feels massive.
FAQ
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.
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