What is Gemini 3.1 Flash-Lite?
Gemini 3.1 Flash-Lite is a lightweight flagship model launched by Google, focused on ultimate cost-effectiveness. With an output speed of 363 tokens per second and an input price of $0.25 per million tokens, it crushesGPT-5 mini(5x faster), at one quarter of the price ofClaude 4.5 Haiku. The model surpasses several larger models on reasoning and multimodal benchmarks such as GPQA Diamond and MMMU-Pro, with an Elo score of 1432, on par with o3. Gemini 3.1 Flash-Lite supports adjustable thinking depth and is suited for high-frequency translation, content moderation, real-time UI generation, and other scenarios. It is currently available in open preview throughGoogle AI Studioand Vertex AI.
Main features of Gemini 3.1 Flash-Lite
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Text generation and understanding: Supports high-quality article writing, summarization, Q&A dialogue, and complex instruction following, with extremely fast response times.
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Multimodal processing: The model can simultaneously understand and process text, images, video, audio, and PDF documents, enabling cross-modal information conversion and analysis.
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Code generation and assistance: Can generate code based on natural language descriptions, supports multiple programming languages, and helps developers quickly build application prototypes.
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Real-time UI and data visualization: Instantly generates user interface prototypes and dynamic data dashboards based on requirements, greatly reducing front-end development costs.
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Adjustable reasoning depth: Provides multiple levels of thinking modes, allowing developers to flexibly choose shallow fast responses or deep reasoning analysis based on task complexity.
Technical principles of Gemini 3.1 Flash-Lite
- Sparse mixture-of-experts architecture: Gemini 3.1 Flash-Lite adopts a sparse mixture-of-experts architecture, achieving efficient inference by dynamically activating a subset of parameters, significantly reducing computational cost while maintaining performance.
- Attention mechanism optimization: The model is optimized for high-throughput scenarios, using advanced attention mechanism optimization techniques to reduce memory usage for long-sequence processing, thereby achieving a generation speed of hundreds of tokens per second.
- Unified multimodal encoding: Multimodal capabilities stem from a unified encoder design, which can map different modal data such as text, images, and video into the same semantic space for joint understanding.
- Adaptive computation mechanism: The model introduces an adaptive computation mechanism that dynamically allocates inference resources based on task difficulty, quickly outputting on simple tasks and enabling deep chains of thought on complex tasks, balancing efficiency and quality.
Project address of Gemini 3.1 Flash-Lite
- Project official website：https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-flash-lite/
Product pricing of Gemini 3.1 Flash-Lite
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Input: $0.25 / million tokens
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Output: $1.50 / million tokens
Application scenarios of Gemini 3.1 Flash-Lite
- High-frequency content processing: Suitable for large-scale text translation, content moderation, data classification, and other scenarios, processing massive requests with extremely low cost and millisecond-level response, supporting content governance pipelines for e-commerce platforms and social media.
- Real-time interactive applications: Powers chatbots, intelligent customer service, and real-time recommendation systems, achieving near-instant user feedback with an output speed of 363 tokens/s, creating a smooth conversational experience.
- Multimodal content conversion: It can quickly convert unstructured content such as PDFs, images, videos, and audio into structured Markdown format, and is widely used in document digitization, media asset management, and knowledge base construction.
- Intelligent Interface Generation: Developers only need a natural language description to generate a complete e-commerce page prototype, data visualization dashboard, or management backend interface within seconds, significantly lowering the barrier to front-end development.