Vision Understanding
Processes and interprets image inputs alongside text, enabling tasks like image captioning, visual question answering, and scene description.
Google
Gemini 2.0 Flash-Lite Vision
Gemini 2.0 Flash-Lite Vision is a multimodal model developed by Google, designed to process both visual and textual inputs. It belongs to the Gemini 2.0 Flash family and is positioned as the fastest and most cost-efficient option within that lineup. The model supports a context window of over one million tokens, making it suitable for tasks that require processing large amounts of information in a single request. It was trained on data up to June 2024. This model is intended as an upgrade path for users of Gemini 1.5 Flash who want improved output quality without changes to cost or latency. Its vision capabilities allow it to handle image understanding tasks alongside text-based workflows. The combination of speed, large context support, and multimodal input handling makes it well-suited for applications such as document analysis, image captioning, and high-throughput pipelines where cost efficiency is a priority.
Feb 25, 2025
1,048,576 context
8,192 tokens output
Vision Understanding
Large Context Window
Multimodal Input
High-Speed Inference
Text Generation
Document Analysis
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 25, 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-Lite Vision
A fuller summary of positioning, capabilities, and source-specific details for Gemini 2.0 Flash-Lite Vision.
Gemini 2.0 Flash-Lite Vision is a multimodal model developed by Google, designed to process both visual and textual inputs. It belongs to the Gemini 2.0 Flash family and is positioned as the fastest and most cost-efficient option within that lineup. The model supports a context window of over one million tokens, making it suitable for tasks that require processing large amounts of information in a single request. It was trained on data up to June 2024.
This model is intended as an upgrade path for users of Gemini 1.5 Flash who want improved output quality without changes to cost or latency. Its vision capabilities allow it to handle image understanding tasks alongside text-based workflows. The combination of speed, large context support, and multimodal input handling makes it well-suited for applications such as document analysis, image captioning, and high-throughput pipelines where cost efficiency is a priority.
Capabilities
What Gemini 2.0 Flash-Lite Vision supports
Large Context Window
Supports up to 1,048,576 tokens in a single context, allowing long documents, multi-image inputs, or extended conversations to be processed together.
Multimodal Input
Accepts combinations of text and image inputs in a single request, enabling workflows that mix visual and textual data.
High-Speed Inference
Optimized for low-latency responses, making it suitable for real-time or high-throughput production applications.
Text Generation
Generates coherent text responses based on visual and textual prompts, supporting summarization, Q&A, and content extraction tasks.
Document Analysis
Can process long-form documents or multi-page inputs within its million-token context window, extracting structured information or answering questions about content.
Pricing for Gemini 2.0 Flash-Lite Vision
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.08
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 |
|---|---|
|
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.
Compare Gemini 2.0 Flash-Lite Vision with related models
Jump straight into the most relevant side-by-side comparison pages for this model.
Gemini 2.5 Pro vs Gemini 2.0 Flash-Lite Vision
Compare Gemini 2.5 Pro and Gemini 2.0 Flash-Lite Vision across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Gemini 2.0 Flash-Lite Vision vs Gemini 2.5 Flash Vision
Compare Gemini 2.0 Flash-Lite Vision and Gemini 2.5 Flash Vision across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Gemini 2.5 Flash Lite vs Gemini 2.0 Flash-Lite Vision
Compare Gemini 2.5 Flash Lite and Gemini 2.0 Flash-Lite Vision across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Gemini 2.0 Flash-Lite Vision vs Gemini 2.5 Flash Image
Compare Gemini 2.0 Flash-Lite Vision and Gemini 2.5 Flash Image across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Gemini 2.0 Flash-Lite Vision vs Gemini 2.5 Flash
Compare Gemini 2.0 Flash-Lite Vision and Gemini 2.5 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Gemini 1.5 Pro Vision Deprecated vs Gemini 2.0 Flash-Lite Vision
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 2.0 Flash-Lite Vision across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus long-context workloads.
FAQ
Common questions about Gemini 2.0 Flash-Lite Vision
What is the context window size for Gemini 2.0 Flash-Lite Vision?
Gemini 2.0 Flash-Lite Vision supports a context window of 1,048,576 tokens, allowing very large inputs to be processed in a single request.
What is the knowledge cutoff date 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 types of inputs does Gemini 2.0 Flash-Lite Vision accept?
The model accepts both image and text inputs, making it a multimodal model capable of handling visual understanding tasks alongside standard text-based prompts.
Who is this model intended for?
According to Google's description, it is designed as an upgrade path for Gemini 1.5 Flash users who want better output quality at the same price and speed.
Where can I access or deploy Gemini 2.0 Flash-Lite Vision?
The model is available through Google Cloud's Vertex AI platform. Documentation for deployment and usage can be found at the official Vertex AI documentation page.
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