Gemini 3.7 Flash

Pricing depends on which provider you use and the pricing model — input versus output tokens, batch versus real-time, and any long-context surcharges. Because rates change frequently, check the pricing table on this page for current per-provider figures rather than relying on a fixed number.

It is aimed at high-volume, latency-sensitive tasks that still need large inputs: document and transcript summarization, structured data extraction, classification and routing, media understanding across image, video, and audio, and tool-calling agent steps. Save the hardest reasoning tasks for a Pro-tier model.

Flash is Google's lightweight tier, tuned toward throughput and cost efficiency, while Pro-tier Gemini models target deeper reasoning and more complex agentic work. Both share the Gemini family's long-context and multimodal design, so many prompts port between them, but you should expect Pro tiers to handle harder problems and Flash to handle more requests per unit of spend.

Yes — our metadata confirms text, image, video, and audio inputs. That allows a single API call to reason over a video clip or audio recording alongside written instructions, which can remove separate transcription or frame-sampling steps from a pipeline.

Tool calling is supported, so the model can be connected to external functions, APIs, and databases within agent frameworks. Specific parameters such as parallel calls or strict schema enforcement depend on the provider's API implementation, so check the provider documentation you plan to use.

The context window is 1,048,576 tokens, roughly one million. That is enough for very long documents, multi-hour transcripts, or large portions of a codebase in one prompt, though filling the window increases both latency and token consumption, so most production deployments still trim inputs to what is needed.