Gemini 2.5 Flash is a balanced, high-throughput AI model that combines strong reasoning with low latency and cost efficiency. It accepts multimodal inputs (text, images, audio, and video) and generates high-quality text responses, making it practical for products that must handle diverse data types at scale. The model is a “thinking model,” so it can surface its chain-of-thought-style reasoning to improve transparency and answer accuracy; developers can control this reasoning depth via an API parameter to tune speed versus thoroughness. Flash-Lite is optimized for the lowest latency and cost and has thinking turned off by default for maximum throughput, while other Flash variants allow more thinking when higher answer quality is required. Native integrations (Google Search grounding, URL context, function calling, and code execution) help the model deliver context-aware and actionable outputs. A Live API preview adds low-latency bidirectional voice and video capabilities for real-time conversational applications. With an expanded context window (up to 1 million tokens), Gemini 2.5 Flash can maintain long conversations or process very long documents without losing coherence. Practical uses include large-scale classification and summarization pipelines, multimodal assistants that interpret images or audio alongside text, interactive voice/video systems for customer-facing applications, and cost-sensitive coding or reasoning tasks where a balance between performance and expense matters. Compared to 2.5 Pro, Flash prioritizes price-performance: it’s not the top option for the most complex coding or advanced reasoning workloads, but it offers excellent real-world value for high-volume, latency-sensitive deployments. Note that some Live API features are in preview and that enabling the model’s thinking improves quality for complex tasks but increases compute and latency.