Gemini 3.1 Pro
Google's most capable multimodal model with deep reasoning and 1M token context.
Gemini 3.1 Pro 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 $2.00 per million input tokens.
- Pricing
- $2.00 / 1M in · $12.00 / 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 3.1 Pro 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 digital bridge that allows two separate pieces of software to share information securely. When you buy something online, an API connects the shopping website directly to your bank to verify your payment behind the scenes without exposing private account 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.
A two-person startup should absolutely use a hosted authentication provider rather than building their own. With only two people, your most precious resource is engineering time, which must be ruthlessly focused on building the unique core features that actually attract customers. Hosted solutions handle the immense complexity of security, password resets, multi-factor authentication, and compliance out of the box, drastically reducing your time to market and minimizing the risk of a catastrophic data breach. However, the single strongest argument against using a hosted provider is the long-term risk of aggressive vendor lock-in combined with exorbitant pricing at scale. Once your user base grows significantly, these platforms often enforce steep pricing tiers that can suddenly cripple your margins, and migrating away from an entrenched third-party authentication system later requires a massively disruptive and expensive engineering overhaul.
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.
Artificial intelligence is transforming our industry, but budgeting for it remains a significant challenge. Unlike traditional software with fixed licensing fees, AI spending is inherently unpredictable for three main reasons. First, costs are directly tied to usage through consumption models based on processing volume. If our employees or customers adopt an AI tool more rapidly than anticipated, our computing bills will spike exponentially overnight. Second, the technology evolves at a rapid pace. Maintaining a competitive edge requires constantly upgrading to newer, more advanced models, which often carry higher price tags and require unforeseen integration work. Third, the hidden costs of data preparation frequently blindside organizations. AI systems require massive amounts of clean, structured data, leading to unexpected expenses in data storage, processing infrastructure, and manual curation. Fortunately, we can regain financial control through strategic adjustments. We must transition from rigid annual budgets to flexible, rolling financial plans that can adapt to technological shifts. Additionally, we need to implement strict usage limits and monitoring tools across our departments to prevent runaway computing costs. Finally, by mandating continuous return on investment evaluations for every AI initiative, we ensure that rising expenses are always justified by measurable business value and consistent operational growth throughout the upcoming fiscal year.
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 ↗