What is Google Gemini? What you need to know

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 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 new 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, 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. 

Right now, Google has the following Gemini models—though this is changing rapidly. 

Gemini 3.5 Flash

Gemini 3.5 Flash is the newest Gemini 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 some agentic, coding, and other benchmarks while working much faster. 

It’s currently available through the API, Gemini chatbot, Gemini for Google Workspace, and lots of other features.

Gemini 3.1 Pro

Gemini 3.1 Pro is Google’s most advanced flagship model. 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, Gemini chatbot, Google AI Search, Gemini for Google Workspace, and other Google tools. It’s likely to be replaced by Gemini 3.5 Pro soon, which is currently in testing.

Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite is designed for cost efficiency and high throughput. It’s available through the API. It will presumably be replaced by Gemini 3.5 Flash-Lite in the coming months.

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 3.5 and 3.1 models, there are a few other Gemini models worth noting:

• Gemini 3 Pro. The previous flagship model; it’s been discontinued in favor of 3.1 Pro.

• 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 replaced by later versions.

• Gemini 1.0 Ultra. Gemini Ultra was Gemini’s largest and most powerful model when it was announced. It was never widely released, though there are persistent rumors that it will get an upgrade.

• Gemini 1.0 Nano. A small model designed for on-device operations, it seems to have been supplanted by Flash but may well be brought back at some point.

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, on the various benchmarks, Gemini 3.1 Pro is currently sitting in 3rd on the Intelligence and Coding leaderboards, and 15th on the Agentic leaderboard. Gemini 3.5 Flash is a very respectable 6th on the Intelligence leaderboard, 12th on the Coding leaderboard, and 3rd on the Agentic leaderboard. In short, the latest Gemini models remain competitive with the best models from other labs.

And when it was released, Gemini 3.5 Flash was the highest-scoring model we’ve ever tested on AutomationBench, Zapier’s benchmarking tool for measuring how well AI handles real, complex business workflows.

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’s also a $200/month AI Ultra Plan for power users.

• 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, and after a few bad years, it’s finally caught up with its competitors. 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 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 Google Gemini 3.1 Pro, 3.5 Flash, and other models through Google AI Studio or Vertex AI. And with Zapier’s Google Vertex AI and Google AI Studio integrations, you can access the latest Gemini models from all the apps you use at work. Learn more about how to automate Google AI Studio.

Related reading:

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