Google’s trying to make waves with Gemini, its flagship suite of generative AI models, apps, and services. But what’s Gemini? How can you use it? And how does it stack up to other generative AI tools such as OpenAI’s ChatGPT, Meta’s Llama, and Microsoft’s Copilot?
To make it easier to keep up with the latest Gemini developments, we’ve put together this handy guide, which we’ll keep updated as new Gemini models, features, and news about Google’s plans for Gemini are released.
What is Gemini?
Gemini is Google’s long-promised, next-gen generative AI model family. Developed by Google’s AI research labs DeepMind and Google Research, it comes in several flavors:
- Gemini Ultra, a very large model.
- Gemini Pro, a large model — though smaller than Ultra. The latest version, Gemini 2.0 Pro, is Google’s current flagship.
- Gemini Flash, a speedier, “distilled” version of Pro.
- Gemini Flash-Lite, a slightly smaller and faster version of Gemini Flash.
- Gemini Flash Thinking, a model with “reasoning” capabilities.
- Gemini Nano, two small models: Nano-1 and the slightly more capable Nano-2, which is meant to run offline.
All Gemini models were trained to be natively multimodal — that is, able to work with and analyze more than just text. Google says they were pre-trained and fine-tuned on a variety of public, proprietary, and licensed audio, images, and videos; a set of codebases; and text in different languages.
This sets Gemini apart from models such as Google’s own LaMDA, which was trained exclusively on text data. LaMDA can’t understand or generate anything beyond text (e.g., essays, emails, and so on), but that isn’t necessarily the case with Gemini models. For example, the latest versions of Gemini Flash and Gemini Pro can natively output images and audio in addition to text.
We’ll note here that the ethics and legality of training models on public data, in some cases without the data owners’ knowledge or consent, are murky. Google has an AI indemnification policy to shield certain Google Cloud customers from lawsuits should they face them, but this policy contains carve-outs. Proceed with caution — particularly if you’re intending on using Gemini commercially.
What’s the difference between the Gemini apps and Gemini models?
Gemini is separate and distinct from the Gemini apps on the web and mobile (formerly Bard).
The Gemini apps are clients that connect to various Gemini models and layer a chatbot-like interface on top. Think of them as front ends for Google’s generative AI, analogous to ChatGPT and Anthropic’s Claude family of apps.
Gemini on the web lives here. On Android, the Gemini app replaces the existing Google Assistant app. And on iOS, the Google and Google Search apps serve as that platform’s Gemini clients.
On Android, users can bring up a Gemini overlay to ask questions about what’s on their screen (for example, a YouTube video). Pressing and holding a supported smartphone’s power button or saying, “Hey Google” summons the overlay.
Gemini apps can accept images as well as voice commands and text — including files like PDFs, either uploaded or imported from Google Drive — and generate images. As you’d expect, conversations with Gemini apps on mobile carry over to Gemini on the web and vice versa if you’re signed in to the same Google Account in both places.
Gemini Advanced
The Gemini apps aren’t the only means of recruiting Gemini models’ assistance with tasks. Slowly but surely, Gemini-imbued features are making their way into staple Google apps and services like Gmail and Google Docs.
To take advantage of most of these, you’ll need the Google One AI Premium Plan. Technically a part of Google One, the AI Premium Plan costs $20 a month and provides access to Gemini in Google Workspace apps like Docs, Maps, Slides, Sheets, Drive, and Meet. It also enables what Google calls Gemini Advanced, which brings the company’s more sophisticated Gemini models to the Gemini apps.
Gemini Advanced users get extras here and there, too, like priority access to new features and models; the ability to run and edit Python code directly in Gemini; and increased limits for NotebookLM, Google’s tool that turns PDFs into AI-generated podcasts. Recently, Gemini Advanced gained a memory feature that stores users’ preferences and allows Gemini to refer to old conversations as context for current chats.
One of the more compelling Gemini Advanced exclusives, Deep Research, leverages Gemini models with “advanced reasoning” to create detailed briefs. In response to a prompt (e.g. “How should I redesign my kitchen?”), Deep Research develops a multi-step research plan and searches the web to craft a comprehensive answer.
Gemini in Gmail, Docs, Chrome, dev tools, and more
In Gmail, Gemini lives in a side panel that can write emails and summarize message threads. You’ll find the same panel in Docs, where it helps write and refine content and brainstorm new ideas. Gemini in Slides generates slides and custom images. And Gemini in Google Sheets tracks and organizes data, creating tables and formulas.
Gemini is in Google Maps, where it can aggregate reviews about local businesses and offer recommendations like how to spend a day visiting a foreign city. The chatbot’s reach extends to Drive, as well, where it can summarize files and folders and give quick facts about a project.
Gemini recently came to Google’s Chrome browser in the form of an AI writing tool. You can use it to write something completely new or rewrite existing text; Google says it’ll consider the web page you’re on to make recommendations.
Elsewhere, you’ll find hints of Gemini in Google’s database products, cloud security tools, and app development platforms (including Firebase and Project IDX), as well as in apps like Google Photos (where Gemini handles natural language search queries), YouTube (where it helps brainstorm video ideas), and Meet (where it translates captions).
Code Assist (formerly Duet AI for Developers), Google’s suite of AI-powered assistance tools for code completion and generation, is offloading heavy computational lifting to Gemini. So are Google’s security products underpinned by Gemini, like Gemini in Threat Intelligence, which can analyze large portions of potentially malicious code and let users perform natural language searches for ongoing threats or indicators of compromise.
Gemini extensions and Gems
Gemini Advanced users can create Gems, custom chatbots on desktop and mobile powered by Gemini models. Gems can be generated from natural language descriptions — for instance, “You’re my running coach. Give me a daily running plan” — and shared with other users or kept private.
The Gemini apps can tap into Google services via what Google calls “Gemini extensions.” Gemini integrates with Drive, Gmail, YouTube, and more to respond to queries such as “Could you summarize my last three emails?”
Gemini Live in-depth voice chats
An experience called Gemini Live allows users to have “in-depth” voice chats with Gemini. It’s available in the Gemini apps on mobile and the Pixel Buds Pro 2, where it can be accessed even when your phone’s locked.
With Gemini Live enabled, you can interrupt Gemini while the chatbot’s speaking to ask a clarifying question, and it’ll adapt to your speech patterns in real-time. Live is also designed to serve as a virtual coach of sorts, helping you rehearse for events, brainstorm ideas, and so on. For instance, Live can suggest which skills to highlight in an upcoming job interview and give public speaking pointers.
You can read our review of Gemini Live here.
Gemini for teens
Google offers a teen-focused Gemini experience for students.
The teen-focused Gemini has “additional policies and safeguards,” including a tailored onboarding process and an AI literacy guide. Otherwise, it’s nearly identical to the standard Gemini experience, down to the “double-check” feature that looks across the web to see if Gemini’s responses are accurate.
What can the Gemini models do?
Because Gemini models are multimodal, they can perform a range of multimodal tasks, from transcribing speech to captioning images and videos in real-time. Many of these capabilities have reached the product stage, and Google is promising much more in the not-too-distant future.
Of course, Google offers no fix for some of the underlying problems with generative AI technology today, like its encoded biases and tendency to make things up (i.e., hallucinate). Neither do its rivals, but it’s something to keep in mind when considering using or paying for Gemini.
Gemini Pro’s capabilities
Google says that its latest Pro model, Gemini 2.0 Pro, is its best yet for coding and complex prompts. 2.0 Pro outperforms its predecessor, Gemini 1.5 Pro, in benchmarks measuring programming, reasoning, math, and factual accuracy.
In Google’s Vertex AI platform, developers can customize Gemini Pro to specific contexts and use cases via a fine-tuning or “grounding” process. For example, Pro (along with other Gemini models) can be instructed to use data from third-party providers like Moody’s, Thomson Reuters, ZoomInfo, and MSCI, or source information from corporate datasets or Google Search instead of its wider knowledge bank. Gemini Pro can also be connected to external, third-party APIs to perform particular actions, like automating a back-office workflow.
Google’s AI Studio platform offers templates for creating structured chat prompts with Pro. Developers can control the model’s creative range and provide examples to give tone and style instructions — and also tune Pro’s safety settings.
Gemini Flash is lightweight, while Gemini Flash Thinking adds reasoning
Gemini 2.0 Flash, which can use tools like Google Search and interact with external APIs, outperforms some of the larger Gemini 1.5 models on benchmarks measuring coding and image analysis. An offshoot of Gemini Pro, Flash is small and efficient — built for narrow, high-frequency generative AI workloads.
Google says that Flash is particularly well-suited for tasks like summarization and chat apps, plus image and video captioning and data extraction from long documents and tables. Meanwhile, Gemini 2.0 Flash-Lite, a more compact version of Flash, outperforms Gemini 1.5 Flash but runs at the same price and speed, according to Google.
Last December, Google released a “thinking” version of Gemini 2.0 Flash that’s capable of “reasoning.” The AI model takes a few seconds to work backward through a problem before it gives an answer, which can improve its reliability.
Gemini Nano can run on your phone
Gemini Nano is a tiny version of Gemini efficient enough to run directly on (some) devices instead of sending the task off to a server somewhere. So far, Nano powers a couple of features on the Pixel 8 Pro, Pixel 8, Pixel 9 Pro, Pixel 9, and Samsung Galaxy S24, including Summarize in Recorder and Smart Reply in Gboard.
The Recorder app, which lets users push a button to record and transcribe audio, includes a Gemini-powered summary of recorded conversations, interviews, presentations, and other audio snippets. Users get summaries even if they don’t have a signal or Wi-Fi connection — and in a nod to privacy, no data leaves their phone in process.
Nano is also in Gboard, Google’s keyboard replacement. There, it powers Smart Reply, which helps to suggest the next thing you’ll want to say when having a conversation in a messaging app such as WhatsApp.
A future version of Android will tap Nano to alert users to potential scams during calls. The new weather app on Pixel phones uses Gemini Nano to generate tailored weather reports. And TalkBack, Google’s accessibility service, employs Nano to create aural descriptions of objects for low-vision and blind users.
Gemini Ultra, MIA for now
We haven’t seen much of Gemini Ultra in recent months. The model isn’t available in the Gemini apps, and it isn’t listed on Google’s Gemini API pricing page. However, that doesn’t mean Google won’t bring Ultra back at some point in the future.
How much do the Gemini models cost?
Gemini 1.5 Pro, 1.5 Flash, 2.0 Flash, and 2.0 Flash-Lite are available through Google’s Gemini API for building apps and services. They’re pay-as-you-go. Here’s the base pricing — not including add-ons — as of February 225:
- Gemini 1.5 Pro: $1.25 per 1 million input tokens (for prompts up to 128K tokens) or $2.50 per 1 million input tokens (for prompts longer than 128K tokens); $5 per 1 million output tokens (for prompts up to 128K tokens) or $10 per 1 million output tokens (for prompts longer than 128K tokens)
- Gemini 1.5 Flash: 7.5 cents per 1 million input tokens (for prompts up to 128K tokens), 15 cents per 1 million input tokens (for prompts longer than 128K tokens), 30 cents per 1 million output tokens (for prompts up to 128K tokens), 60 cents per 1 million output tokens (for prompts longer than 128K tokens)
- Gemini 2.0 Flash: 10 cents per 1 million input tokens, 40 cents per 1 million output tokens. For audio, 70 cents per 1 million input tokens.
- Gemini 2.0 Flash-Lite: 7.5 cents per 1 million input tokens, 30 cents per 1 million output tokens.
Tokens are subdivided bits of raw data, like the syllables “fan,” “tas,” and “tic” in the word “fantastic”; 1 million tokens is equivalent to about 750,000 words. Input refers to tokens fed into the model, while output refers to tokens that the model generates.
2.0 Pro pricing has yet to be announced, and Nano is still in early access.
Is Gemini coming to the iPhone?
It might.
Apple has said that it’s in talks to put Gemini and other third-party models to use for a number of features in its Apple Intelligence suite. Following a keynote presentation at WWDC 2024, Apple SVP Craig Federighi confirmed plans to work with models, including Gemini, but he didn’t divulge any additional details.
This post was originally published February 16, 2024, and is updated regularly.
