What is Google Gemini?

Google has been in its "Gemini era" for a couple years now, and while the confusing rebrandings have slowed, everything else continues to improve at a rapid pace. Gemini is the name Google gave to its current generation family of multimodal AI models, but in typical Google fashion, it also applies to basically everything else that's related to AI.

It can get a touch confusing since, by my reckoning, Google has:

• Google Gemini, a family of multimodal AI models. The newest is Gemini 4, though some older model families are still around. This is what Google uses in its own apps and to power AI features on its devices, but developers can integrate it in their apps, too.

• Google Gemini, a chatbot that runs on the Gemini family of models. (This is the chatbot that used to be called Bard, one of the aforementioned confusing rebrands.)

• Google Gemini, a replacement for Google Assistant on Android phones and tablets, Wear OS watches, and Android Auto. Google is bringing Gemini to Google TV and Google Home speakers, too, but those still use Assistant for now.

• Gemini for Google Workspace, the AI features integrated across Gmail, Google Docs, and the other Workspace apps for paying users. 

• And a few more Geminis that I'm sure I'm missing. 

All of these new Geminis are based around the core family of multimodal AI models, so let's start there.

Table of contents:

• What is Google Gemini?

• Google Gemini models come in multiple sizes

• How does Google Gemini compare to other LLMs?

• How does Google use Gemini?

• Google Gemini is designed to be built on top of

• How to access Google Gemini

What is Google Gemini?

Google Gemini is a family of AI models, like OpenAI's GPT. They're all multimodal models, which means they can understand and generate text like a regular large language model (LLM), but they can also natively understand, operate on, and combine other kinds of information like images, audio, videos, and code.

For example, you can give Gemini a prompt like "what's going on in this picture?" and attach an image, and it will describe the image and respond to further prompts asking for more complex information. Similarly, if you give it a load of data, it can generate a graph or other visualization; or it can help you interpret charts, read signs, or translate menus. The recently launched Gemini Omni models take that even further and allow you to create "anything from any input," though they're starting with creating video from text, image, audio, and video prompts.

Because we're now deep in the corporate competition era of AI, most companies are keeping pretty quiet on the specifics of how their models work and differ. Still, Google has confirmed that the Gemini models use a transformer architecture and rely on strategies like pretraining and fine-tuning, much as other major AI models do. The larger Gemini models have also shifted to a mixture-of-experts approach, which allows them to operate more efficiently with larger parameter counts.

The latest Gemini models hit all the state-of-the-art bases. While other model families have caught up, Google pioneered long context windows with Gemini. This means that a prompt can include more information to better shape the responses the model is able to give and what resources it has to work with. Right now, nearly every current model in the Gemini family has at least a one million token context window. That's enough for multiple long documents, large knowledge bases, and other text-heavy resources. If you have to parse a complicated contract, you could upload the whole document to Gemini and ask questions about it—no matter how long it is. This is also useful if you're building a retrieval augmented generation (RAG) pipeline, though your API costs would be very high if you actually used the full context window in production. 

Similarly, all the modern Gemini models are capable of reasoning, though Google calls it "thinking." This is what makes them able to work through hard logic problems, accurately understand scientific information, and generate code. This last point is particularly relevant given the rise in vibe coding and otherwise using AI to build applications.

Tool use and agentic features are also a big part of the latest Gemini models. This falls a little bit outside how regular people use these models in chatbots, but for developers and power users, it allows them to create AI applications that can take independent action.

Google Gemini models come in multiple sizes

The different Gemini models are designed to run on almost any device, which is why Google is integrating it absolutely everywhere. Google claims that its different versions are capable of running efficiently on everything from data centers to smartphones.

Each Gemini model differs in how many parameters it has and, as a result, how good it is at responding to more complex queries as well as how much processing power it needs to run. Unfortunately, figures like the number of parameters any given model has are often kept secret—unless there's a reason for a company to brag. 

Google is also complicating things by being Google. Gemini 3.5 Pro was promised and never shipped, and Google now says it won't release it at all. Instead, it moved on to Gemini 4, starting with Gemini 4 Argon. In the meantime, Google's Flash models have outperformed its older Pro model on many benchmarks despite technically being a lower tier in the family.

Anyway, inside baseball aside, Google currently has the following Gemini models—though this is changing rapidly.

Gemini 4 Argon

Gemini 4 Argon is Google's new frontier model and the first in the Gemini 4 family. It's built for long, complex work: real-world software engineering, enterprise knowledge work like legal and finance tasks, and cybersecurity defense. It's set a bunch of benchmark records, including on Zapier's AutomationBench—at its release, it was the top model in five of the six business domains we test.

The catch is that you can't really use it yet. Argon is rolling out first to trusted cyber defenders through Google's Fairwind Program, and Google says it'll open up to developers, enterprises, and consumers as soon as possible, starting with paid API customers and Google AI Ultra subscribers. It's also pricier than Gemini's Flash models. It's launching at $2 per million input tokens and $10 per million output tokens, but it'll increase to $4 and $20 after the introductory period.

Gemini 3.8 Flash

Gemini 3.8 Flash is Google's newest Flash model, and despite the "Flash" branding, it's not meant to be a fast and cheap alternative to the Pro model; it's a frontier model in its own right. It has a 1M token context window, supports reasoning, and outscores 3.1 Pro on many benchmarks while working much faster and at lower cost. It's also designed to work more efficiently and use less tokens, which amplifies both the speed and cost benefits compared to Flash 3.7 and older models. 

It's available through the Gemini API (in Google AI Studio and Android Studio), Google Antigravity, Stitch, and Gemini Enterprise Agent Platform. In Google's own products, it's available to Google AI Pro and Ultra subscribers in the Gemini app, AI Mode in Search, and Gemini in Google Sheets.

Gemini 3.8 Flash Cyber

Gemini 3.8 Flash Cyber is a cybersecurity model built on top of 3.8 Flash. It's designed to help defenders find, verify, and fix vulnerabilities. It's only available to governments, critical infrastructure operators, and other trusted partners through Google's Fairwind Program, where it's paired with CodeMender, though Google says it will expand partner access over time.

Gemini 3.1 Pro

Gemini 3.1 Pro is one of Google's most advanced models, but it's no longer the flagship. It has a 1M token context window and is capable of reasoning. It's especially good at coding and responding to complex prompts. It's currently available through the API (where Google still labels it a preview), Gemini chatbot, Google AI Search, Gemini for Google Workspace, and other Google tools. Google planned to replace it with Gemini 3.5 Pro, but it scrapped that model, and Gemini 4 Argon is now Google's flagship.

Gemini 3.5 Flash-Lite

Gemini 3.5 Flash-Lite is designed for cost efficiency and high throughput. 3.5 is better at agentic tasks and coding, so is more suitable for agentic systems than earlier models. It also now has computer use.

Gemini 3.5 Flash-Lite is available through the API. It's also rolling out in Google Search.

Gemini Omni Flash

Gemini Omni Flash is the first in a new family of Gemini models. It currently allows you to create and edit videos with text, image, video, and audio prompts, but it will be updated to support creating images and audio as well. It's rolling out to Gemini, Google Flow, and YouTube.

Older Gemini models

In addition to the state-of-the-art Gemini 4 and Gemini 3.8 models, there are a few other Gemini models worth noting:

• Gemini 3.7 Flash. The previous Flash model; it's been superseded by Gemini 3.8 Flash.

• Gemini 2.5 Pro, Flash, and Flash-Lite. The previous generation of models; all have been superseded by Gemini 3 versions.  

• Gemini 2.0 Flash. Previously Google's most widely available model, Gemini 2.0 Flash has now been shut down and replaced by later versions.

• Gemini 1.0 Ultra. Gemini Ultra was Gemini's largest and most powerful model when it was announced. It has since been superseded by newer Pro models.

• Gemini 1.0 Nano. The small, on-device model in the family. It runs locally on supported Android phones through Android's AICore, and it's still being updated: Google has previewed Gemini Nano 4, which is built on its open Gemma 4 models.

How does Google Gemini compare to other LLMs?

We've reached the point where directly comparing AI models is increasingly irrelevant. The best models from OpenAI, Anthropic, Meta, Google, DeepSeek, Qwen, and a number of other companies are all incredibly powerful—and how and what you use them for is now significantly more relevant than which model you choose. 

Similarly, the trade-offs between speed and power are becoming more and more important, especially with the high token usage of tasks like coding, and personal agents. There's a reason that model families include multiple models tailored for different situations.

With that said, Google has taken the lead on the benchmarks. At its release, Gemini 4 Argon held the top two spots on Zapier's AutomationBench leaderboard, ahead of Claude Sonnet 5.5, Claude Opus 5.5, and GPT-6 Astra. Argon is also pricier than Google's other models, at $1.70 per task on AutomationBench at its High setting (at standard pricing), and it's only rolling out to a limited group of trusted testers for now, with broader access to come. Until then, Google's Flash models remain its most competitive options in terms of speed and affordability.

How does Google use Gemini?

More than two years into the Gemini Era, Google has integrated (or plans to integrate) AI basically everywhere it can. This list isn't exhaustive as Google is continuing to roll out new features, but let's go through the major Gemini-powered tools:

• Google Gemini (the chatbot). The most obvious place that Google deploys Gemini is with the chatbot-formerly-known-as-Bard. It's also called Gemini and is more of a direct ChatGPT competitor than a replacement for Search. It has a deep research mode, can search the web, and integrates with other apps. If you're deep in Google's ecosystem, it's a great tool. 

• Google Workspace. The other area where Gemini is incredibly prominent is Google's Workspace apps like Gmail, Docs, and Sheets. You need to be a Business Standard subscriber ($16.80/user/month) to get the full power of Gemini across all the different apps, but it can do a lot. Zapier has a full breakdown of all Gemini for Workspace can do, but some of the highlights are summarizing emails in Gmail and files in Google Drive, generating charts and tables in Sheets, and taking notes and translating in Google Meet calls.

• Google AI Plans. For non-business users, the $7.99/month Google AI Plus plan provides access to Gemini Omni and increases the usage limits; the $20/month Google One Google AI Pro plan gets you access to more of Gemini's most advanced models and features in the chatbot. There are also $100/month and $200/month AI Ultra Plans for power users, and anyone using Gemini to write code.

• Google Search. Search is going to keep getting a lot of Gemini-powered updates. Its AI Overviews are basically quick answer boxes for more complex queries. And AI Mode offers more of an actual AI search engine, like Perplexity.

• Android Auto and Gemini for Google TV. Both products received Gemini updates last year.

• Android. Gemini integration continues to roll out for Google's smartphone operating system. 

• Everywhere else. Google has committed hard to AI. Expect to see Gemini in every app Google can add it to—at least until there's another name change. It's even available in Chrome if you really want Gemini absolutely everywhere.

Google Gemini is designed to be built on top of

In addition to using Gemini in its own products, Google also allows developers to integrate Gemini into their own apps, tools, and services. 

It seems that almost every app now is adding AI-based features, and many of them are using OpenAI's models or Anthropic's Claude to do it. Google wants a piece of that action, so Gemini is designed from the start for developers to be able to build AI-powered apps and otherwise integrate AI into their products. The big advantage it has is that it can integrate them through its cloud computing, hosting, and other web services.

Developers can access the latest Gemini models through the Gemini API in Google AI Studio or Google Cloud's Gemini Enterprise Agent Platform (formerly Vertex AI). This allows them to further train Gemini on their own data to build powerful tools like folks have already been doing with GPT models.

How to access Google Gemini

The easiest way to check out Gemini is through the chatbot of the same name. If you subscribe to a Gemini plan, you'll also be able to use it throughout the various different Google apps.

Developers can also test Gemini models through Google AI Studio or Vertex AI. And with Zapier, you can use all the latest Gemini models inside your actual work. Add AI steps into your deterministic workflows to pull in the power of Gemini only when you need it, and work securely across 9,000+ apps.

Learn more about how to integrate AI into all the apps you use at work.

Related reading:

• Interact with your documents using Google's AI-powered NotebookLM

• Security risks of generative AI and how to prepare for them

• The best AI courses for beginners

• Every Gemini app, and what each one can do

• What is Google AI Mode?

• How to connect Gemini Enterprise to the rest of your tech stack

This article was originally published in January 2024. The most recent complete update was in October 2026.