Gemini 2.0 Flash-Lite Vision
Gemini 2.0 Flash-Lite is our fastest and most cost efficient Flash model. It's an upgrade path for 1.5 Flash users who want better quality for the same price and speed.
Google Fast, cost-efficient vision model from Google
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.
What Gemini 2.0 Flash-Lite Vision supports
Vision Understanding
Processes and interprets image inputs alongside text, enabling tasks like image captioning, visual question answering, and scene description.
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.
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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 | 72.4% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 53.5% |
| MATH-500 | Undergraduate and competition-level math problems | 87.3% |
| AIME 2024 | American math olympiad problems | 27.7% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 18.5% |
| HLE | Questions that challenge frontier models across many domains | 3.6% |
| SciCode | Scientific research coding and numerical methods | 25.0% |
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.
Parameters & options
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