Gemini 3 Flash
Fast, cost-efficient Gemini model for high-throughput text and multimodal tasks.
Gemini 3 Flash is a cloud text model from Google. It is multimodal: alongside a text prompt it accepts images, video, and audio, then replies with generated text. Its context window handles up to 1M input tokens and up to 66K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions, temperature, and top-p sampling. It runs through Replicate and Runware using your own API key, from $0.50 per million input tokens.
- Pricing
- $0.50 / 1M in · $3.00 / 1M out
- Inputs
- Text, Images, Video, Audio
- Context window
- 1M in · 66K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Examples
Generated with Gemini 3 Flash via Runware. The same three prompts run against every text model in the catalog, shown verbatim — so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a set of rules that lets different computer programs talk to each other. It acts as a messenger, allowing one app to request information or services from another. This allows separate systems to work together and share data without needing to understand how they were built.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
You should use a hosted authentication provider. As a two-person startup, your most valuable asset is time, and building a secure, reliable authentication system from scratch is a massive distraction from your core product. Modern providers handle complex requirements like multi-factor authentication, social logins, and secure password storage out of the box, saving you weeks of development and ongoing maintenance. However, the strongest argument against this approach is the inevitable vendor lock-in. Once your user base grows and you are deeply integrated with a specific provider's API and database structure, migrating to a different service or an in-house solution becomes an expensive, high-risk engineering project that can halt feature development for months. In the long run, you sacrifice control and potentially face significant scaling costs in exchange for immediate speed.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Predicting expenditures in artificial intelligence is notoriously difficult because the underlying economics differ significantly from traditional software. First, AI costs are primarily driven by consumption rather than flat licensing fees. As your teams scale their experiments or as customers interact more frequently with AI-driven features, the backend processing requirements surge unpredictably. Second, the hidden expenses of data preparation are often underestimated. AI models require clean, structured inputs, and the labor-intensive process of refining vast data sets often leads to budget overruns before any insights are generated. Third, the industry’s rapid pace means today’s cutting-edge tools can become obsolete overnight, necessitating frequent and unplanned reinvestments in newer, more efficient models to stay competitive. To gain control over these fluctuating costs, leadership must shift from static annual budgets to dynamic financial monitoring. Implementing automated cost guardrails and usage alerts ensures that a sudden spike in activity does not lead to a massive end-of-month invoice. Furthermore, prioritizing a robust data infrastructure will reduce the friction and surprise costs associated with model deployment. By fostering transparency between technical teams and finance, you can transform AI from a volatile expense into a manageable strategic asset, ensuring that innovation remains sustainable while your bottom line remains protected from unforeseen digital overhead.
Google and Google DeepMind build the Gemini family of multimodal models, the Imagen and Nano Banana image models, the Lyria music models, and the Veo video models.
deepmind.google ↗