What Is gemini ai models? AI Models Explained
Google Gemini comes up constantly in conversations about AI, but it's easy to lose track of what it actually is versus what it's often confused with. Gemini isn't a single app. It's Google's family of AI models that powers a growing set of Google products.
Google Gemini is Google's family of multimodal AI models, meaning it can process and generate text, images, audio, and other formats within a single system. Gemini powers Google's AI products, including the Gemini app, AI features inside Search, and tools like NotebookLM. It comes in multiple versions built for different levels of speed and capability.
Understanding Gemini as a model family, not a single product, makes the rest of Google's AI lineup easier to place.
Gemini as a model family, not one product
"Gemini" refers to the underlying AI models Google builds and trains. Those models then get used inside different Google products and features. This is a similar structure to how other AI labs separate their underlying models from the consumer apps built on top of them.
That distinction matters because:
The Gemini app (formerly Bard) is a consumer chatbot product built on Gemini models
Gemini models also power features inside other Google products, like AI-generated summaries in Search, or Workspace features in Gmail and Docs
Google has released Gemini in multiple size/capability tiers, generally trading off speed against depth of capability, so different products can use a version suited to their needs
Multimodal: what that actually means
Gemini is described as multimodal, which means it's built to work across more than one type of input and output, not just text.
In practice, that generally includes:
Text: reading and generating written language
Images: interpreting images you provide, and in some product contexts, generating images
Audio: processing spoken language or audio content
Video: in some contexts, interpreting video input
This is different from an AI model that only handles text. A multimodal model can, for example, look at a photo and describe it, or work with a mix of text and image input in the same conversation, within a single system rather than routing to separate tools for each format.
What Gemini powers across Google's products
Gemini shows up in more places than most people realize, because Google has been steadily building it into existing products rather than keeping it confined to a standalone chatbot.
The Gemini app: Google's direct consumer-facing chatbot, comparable in purpose to other conversational AI assistants
Google Search's AI features: AI-generated overviews and more conversational search experiences draw on Gemini models
Google Workspace: AI assistance features inside Gmail, Docs, Sheets, and other Workspace apps
NotebookLM: a research and note-taking tool that uses Gemini for tasks like summarizing source material
Deep research features
One notable use of Gemini is in research-oriented features that go beyond a single question-and-answer exchange. These tools are generally designed to take a research question, work through multiple steps or sources, and return a more thorough synthesized answer rather than a quick single response. This kind of multi-step research capability is one of the more advanced applications built on top of Gemini's underlying models.
NotebookLM and audio-style content generation
NotebookLM, Google's note-taking and research tool, includes a feature that can turn uploaded source material into an audio-style discussion format, generally structured like a casual conversation between two hosts summarizing the content. This is another example of a Gemini-powered feature: taking written source material and generating a genuinely different output format from it, rather than just summarizing it as more text.
Why Google built Gemini this way
Building one underlying model family that powers many products, rather than separate AI systems for each product, lets Google:
Improve the core models once and have those improvements propagate across many products
Offer different capability/speed tiers depending on what a given product or task actually needs
Keep a consistent underlying AI "brain" behind features that otherwise look like separate tools to the end user
How Gemini compares to using a single chatbot app
If you're used to thinking of AI tools as individual apps, the more useful mental model for Gemini is: it's infrastructure that shows up inside multiple apps, one of which happens to be a standalone chatbot with the same name.
That means:
You might already be using Gemini-powered features without realizing it, if you use Google Search's AI overviews or Workspace AI tools
The "Gemini app" experience and a Gemini-powered feature inside another product can feel different, even though they draw on the same underlying model family
Capability differences you notice between tasks may come down to which Gemini tier is powering that specific feature
FAQs
Is Google Gemini the same as the old Google Bard?
Bard was Google's original consumer chatbot product, which was renamed to Gemini. The Gemini app is the direct successor, now built on Google's Gemini model family rather than earlier underlying models.
What does "multimodal" mean for Gemini?
It means Gemini can work with more than just text, generally including images, audio, and in some cases video, within a single system rather than needing separate tools for each format.
Do I need a separate subscription to use Gemini?
It depends on which Gemini-powered feature you mean. Some Gemini features are built into free Google products like Search, while more advanced capability, higher usage limits, or specific tools may require one of Google's paid AI subscription plans.