Gemini 2.5 Flash
Balanced Gemini model with strong reasoning and a 1M token context window.
Gemini 2.5 Flash is a cloud text model built by Google. It is multimodal: alongside a text prompt it accepts images and video, 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 using your own API key, from $0.30 per million input tokens.
- Pricing
- $0.30 / 1M in · $2.50 / 1M out
- Inputs
- Text, Images, Video
- Context window
- 1M in · 66K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Examples
Generated with Gemini 2.5 Flash via Replicate. 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 way for different computer programs to talk to each other. It defines the requests and responses they can make, allowing one program to ask another for information or to perform a specific task, without needing to know its internal details.
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.
For your two-person startup, you should unequivocally use a hosted provider for user authentication. Building secure, compliant, and scalable authentication from scratch is a massive, ongoing security and development burden that will divert precious time and resources away from your core product. Hosted solutions provide robust security, easy integration, and handle all the complex edge cases, allowing you to launch faster and focus on what makes your business unique. This frees you from the immense responsibility of patching vulnerabilities, managing infrastructure, and complying with ever-evolving security standards. The single strongest argument against this recommendation is the potential for vendor lock-in and a perceived loss of ultimate control over a critical part of your user experience, which could limit highly specialized customization or future integration flexibility not offered by the provider.
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 our AI spending is challenging due to several inherent complexities. Firstly, AI development is highly iterative and experimental. Unlike traditional software, creating effective AI models often requires numerous training runs, data processing cycles, and algorithm adjustments. Each experiment consumes significant compute resources and specialized personnel time, and many iterations might be necessary before achieving a viable solution, making the overall resource consumption difficult to forecast precisely. Secondly, the underlying resource demands are dynamic. As models increase in complexity or data volumes grow, the necessary computational power for training and ongoing operation can escalate rapidly and unexpectedly, particularly in cloud environments where costs are usage-based. This variable scaling of infrastructure resources creates significant budget uncertainty. Lastly, the AI landscape itself is constantly evolving. New tools, platforms, and talent demands emerge frequently, requiring continuous investment in updated technologies, training, and competitive compensation to attract and retain expert staff, which impacts long-term cost projections. To better manage this, we should carefully focus on linking AI investments to clear, measurable business outcomes and prioritize projects with the highest potential return on investment. Implementing phased project approaches with regular cost reviews and leveraging advanced cost monitoring tools will provide greater visibility and control, enabling more informed decisions and adjustments as projects progress. Building internal expertise will also reduce reliance on expensive external resources.
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 ↗