Google Gemini 4 Argon
Google's flagship reasoning model for complex software engineering, deep mathematical and scientific problem solving and autonomous multi-step agents, with a shared 1M-token context.
Gemini 4 Argon is a cloud text model built by 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 1.05M input tokens and up to 33K 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 Runware, OpenRouter, and Google using your own API key, from $2.00 per million input tokens.
- Released
- Oct 2026
- Pricing
- $2.00 / 1M in · $10.00 / 1M out
- Inputs
- Text, Images, Video, Audio
- Context window
- 1.05M in · 33K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Samples
Generated with Gemini 4 Argon via OpenRouter. 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 like a standard electrical outlet, but for software. You do not need to know how the power grid works to use a lamp; you just plug it in. Similarly, an API lets different apps plug into each other to share information and features effortlessly.
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.
Use a hosted authentication provider. As a two-person team, your scarcest resource is engineering time, and spending it on a solved problem like authentication delays you from building the unique core value of your product. Beyond the initial login screen, rolling your own requires maintaining password resets, multi-factor authentication, session management, and protection against constantly evolving security threats. A hosted provider gives you enterprise-grade security and compliance out of the box in an afternoon, allowing you to focus entirely on finding product-market fit. However, the strongest argument against this approach is the predatory pricing scale and deep vendor lock-in of hosted providers. While cheap or free initially, these services often impose steep price hikes based on monthly active users, meaning a sudden viral surge in free signups could bankrupt your startup right when migrating away is most technically painful and risky.
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 artificial intelligence costs is challenging because AI operates differently than traditional corporate software programs. First, vendors charge based on consumption rather than flat monthly licenses. Every time an employee prompts a tool or a customer interacts with a chatbot, we pay for the exact computing power used, making monthly bills fluctuate wildly with popularity. Second, developing custom solutions requires unpredictable experimentation. Teams must repeatedly train and test models to ensure accuracy, and each failed attempt consumes expensive cloud resources. Third, hidden data preparation costs frequently derail initial budgets. Before any smart system works, staff must clean and organize our messy historical data, a process that always takes longer than anticipated. Fortunately, we can regain financial control through three practical measures. We must implement strict software guardrails that automatically cap daily spending and alert managers before minor experiments become budget crises. Additionally, we should match the tool to the task by routing simple requests to smaller, cheaper models while reserving expensive, advanced systems for complex problems. Finally, establishing a dedicated oversight committee will ensure we only fund projects with clear, measurable business returns. By halting underperforming pilots early and monitoring usage closely, we can harness this technology securely without suffering unpleasant financial surprises each quarter.
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 ↗Google's most capable Flash model, with clear gains over 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning, plus vision and a 1M-token context window.
Google's most efficient multimodal Flash model: an updated reasoning stack with stronger coding and computer-use quality, at the same speed and scale as the rest of the Flash line.
Google's most capable multimodal model with deep reasoning and 1M token context.
Google's updated Nano Banana 2 image model, with sharper graphic composition, steadier subjects across edits and closer prompt following, from text or up to 14 reference images at 1K to 4K.
Ultra-fast image generation model optimised for speed and creative output.
Google's fastest, most cost-efficient Nano Banana model — 1K images in ~4 seconds for high-volume workflows.