What is google gemini?
Executive summary
Google Gemini is a family of multimodal large language models and an end-user assistant that combines text, images, audio, video and code understanding with “agentic” tool use — positioned by Google as its flagship AI for reasoning, planning and content generation [1] [2]. The product spans consumer-facing apps, developer APIs and cloud integrations, with multiple model tiers (Nano, Flash, Pro, Ultra/3 Pro) designed for on-device, cost‑efficient, and high‑capability use cases respectively [3] [2] [4].
1. What Gemini is and how Google describes it
Gemini is presented by Google as a multimodal AI system and an everyday assistant that can help writing, planning, brainstorming, code, and multimedia creation, and is “grounded in Google Search” to support follow‑up queries and context [5] [1]. Google and DeepMind describe successive Gemini releases (including Gemini 2.0 and Gemini 3) as advancing reasoning, multimodal comprehension and agentic abilities — framing Gemini as a platform that can analyze large datasets, codebases and documents with long context windows [2] [4] [6].
2. The technical family: models, tiers and developer access
Gemini is not one monolithic model but a family with specific variants for different needs: smaller on‑device “Nano” models, cost‑optimized “Flash” options, and higher‑capability “Pro” and “Ultra/3 Pro” versions for complex reasoning and tool use; these are exposed through the Gemini app, Gemini API, Google AI Studio and Vertex AI for cloud customers [3] [2] [7]. Google documents model aliases, deprecation schedules (for example Gemini 2.0 Flash deprecation notice), and pricing/latency tradeoffs for developers using the API and cloud services [2] [8].
3. What Gemini can do in practice
Google demonstrates Gemini generating text, images and short videos, synthesizing multimodal inputs, creating interactive visual layouts and simulations for search responses, and connecting to external tools like Search, Maps, code execution and PDFs — enabling workflows from content creation to agentic automation and robotics control via a vision‑language model [1] [9] [7]. DeepMind and Google emphasize improvements in benchmarks for reasoning and coding with newer Gemini releases, claiming substantial task‑solving gains versus prior versions [4] [10].
4. Availability, pricing and tiers for users and enterprises
Gemini is available as a free, unlimited in‑app assistant for many users while higher tiers (Google AI Pro/Ultra or Google One AI plans) unlock Gemini 3 Pro, Deep Research, agentic features, and extended generation capabilities like video and Deep Think modes; enterprises can also access models through Vertex AI and AI Studio [1] [11] [12]. Public reporting notes developer pricing for Flash variants and that Google opened Gemini 2.0 broadly to developers and users as part of its push into agentic AI [8] [2].
5. Competing claims, safety framing and open questions
Google and DeepMind tout benchmark leads and state‑of‑the‑art reasoning (including claims Gemini Ultra outperformed other commercial models on some tests), but independent verification and long‑term safety, alignment and misuse risks remain active areas of scrutiny; Google has emphasized staged rollouts and safety testing in public messaging [3] [4]. Alternatives and critiques point out the competitive landscape — OpenAI, Anthropic, Meta and others are likewise pursuing multimodal, agentic systems — and some outlets and documentation highlight tradeoffs between capability, cost and latency that developers must weigh [8] [13].