Gemini 3.5 Flash is a high-efficiency, multimodal large language model developed by Google, designed to provide frontier-level reasoning and coding capabilities within a low-latency, cost-effective framework. Released as part of the Gemini 3 series, this model is specifically optimized for agentic workflows, parallel execution loops, and long-horizon tasks. It introduces a dynamic "thinking" architecture that allows users to adjust the model's reasoning depth through predefined levels—minimal, low, medium, and high—to balance response quality against computational cost.
The "medium" configuration serves as the standard default for Gemini 3.5 Flash, providing a balanced trade-off between speed and analytical depth. In this mode, the model is capable of handling complex multi-step reasoning, advanced software engineering tasks, and dense document analysis without the higher overhead of "high" effort modes. It natively supports a wide range of input modalities, including text, images, audio, video, and PDFs, allowing it to process diverse data types within a single context window.
Capabilities and Architecture
Gemini 3.5 Flash features a massive 1 million token context window, enabling it to retain vast amounts of information from entire codebases or multi-week conversation histories. It supports advanced features such as structured outputs, native tool use (including Google Search and code execution), and multimodal function responses. Unlike previous generations, Gemini 3.5 Flash is designed for sustained performance in autonomous agent environments, where it can manage sub-agents and iterate on complex problems over extended periods.
Prompting and Usage
For optimal results, users are encouraged to utilize the thinking_level parameter rather than traditional sampling parameters like temperature or top-p, which are no longer recommended for this reasoning-centric architecture. When utilizing the model for agentic loops, developers should leverage the Interactions API to benefit from thought preservation, which carries reasoning context forward across multiple turns. For particularly difficult logic or mathematical problems, switching from the default medium level to high can further enhance accuracy at the cost of increased latency.