Gemini Updates & Release Notes

Building upon the power of Gemini in Sheets on the web, we’re excited to announce that you can now use Gemini in Google Sheets on your Android device to quickly analyze and understand your data while on the go.
Simply tap on the Gemini spark icon, and you can ask questions about your data, generate analytical insights, and create charts. To help you get started, we've included suggested prompts such as “Summarize this table,” and “Analyze for insights.”

(Please note: Mobile capabilities are currently focused on data analysis and insights. To perform complex edits, formatting actions, or generate formulas, please use Gemini in Google Sheets on the web).

Getting started

• Admins: There is no specific admin control for this mobile feature beyond the general Gemini for Google Workspace enablement at the domain/OU level. Visit the Help Center to learn more about managing Gemini access.
• End users: This feature will be available by default for eligible users. To access it, open a compatible spreadsheet on your Android device and tap the Ask Gemini icon. Visit the Help Center to learn more about using Gemini in Google Sheets.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) started on September 9, 2026

Availability

• Business: Business Standard and Plus
• Enterprise: Enterprise Standard and Plus
• Education: Google AI Pro for Education
• Consumer: Google AI Pro and Ultra

Resources

• Google Help: Collaborate with Gemini in Google Sheets

The new Gemini app for Windows lets you access AI help with a simple keyboard shortcut, directly from your desktop.

Today, we're launching the Gemini app for Windows. Designed to work seamlessly alongside your favorite tools and daily applications, the new desktop app gives you instant assistance without breaking your flow.

Here are three ways you can use the Gemini app on your PC:

1. Access Gemini instantly with a keyboard shortcut.

Press Alt + Space on your PC at any time to open Gemini over your active work. Whether you need a quick fact-check on a document or a few catchy title ideas for a presentation, you can get the help you need and jump right back into your workflow.

1. Power through deep work in a dedicated workspace.

Inside the new app, you can access everything you use Gemini for already. Hand off multi-step tasks to Gemini Spark¹, your 24/7 personal AI agent, or ask Gemini to draft a project summary by pulling information directly from your Google apps like Gmail and Google Drive.

1. Create images and videos.

Bring creative concepts to life directly from your desktop. You can generate custom images with Nano Banana for a presentation, direct a high-quality video with Gemini Omni¹, and best of all you can do it all in one convenient place.

Lightweight and quiet, the app runs without slowing down your PC. This is just the beginning for the Gemini app on Windows, with more native desktop capabilities rolling out over time. The app is available today globally for Windows 10 and 11. Download the app at gemini.google/desktop.

Bring Google Gemini to your PC with the new Windows app

• What: The Gemini app is now available on Windows. Press Alt + Space to summon Gemini instantly from anywhere on your PC whenever you need it. You can get quick answers, draft messages and brainstorm ideas right alongside your active applications. Inside your dedicated desktop workspace, you have access to everything that you love about Gemini. You can connect to Google apps like Gmail and Google Drive to summarise documents and find information quickly, generate custom images with Nano Banana and dive into in-depth research on any topic.
• The app is available on Windows 10 or later. Download it today at gemini.google/desktop.
• Why: Your desktop is where you get work done. Bringing Gemini straight to your PC with instant shortcut access keeps you in your flow, making it simple to get answers and create visuals right alongside your everyday tools.

Completing a single task shouldn't mean breaking your focus or switching apps

To keep you in the flow of work, Gemini can tackle complex tasks behind the scenes, working as an intelligent orchestrator across Workspace using the power of Workspace Intelligence. You can prompt Gemini to help from wherever you are working, including Google Chat, Drive, Docs, Slides, and Gmail*.

Before, you had to prompt within each individual app to get personalized AI help. For example, generating a personalized document with Gemini required being in Docs while building AI-powered spreadsheets required being in Sheets. Now, for example, with richer AI integrations across Workspace apps, Gemini can help you drive your work forward faster by creating personalized and formatted docs or beautifully designed slides without leaving Gmail. This functionality also will soon power the flows you build in Workspace Studio.

Now you can use Gemini across your Workspace apps to:

• Create content: Generate formatted Google Docs, structured Sheets, or stylized Slides in the background, saved securely to your Drive.
  • Example prompts:
    • When in Gmail (via the side panel) to create a doc: “Create a strategy brief for this project with goals, milestones and next steps.“
    • When in Gmail (via the side panel) to create a spreadsheet: "Create a tracker for this project in a new spreadsheet"
    • When in Docs (via the side panel) to create a deck: "Turn this proposal into an easy to read slide deck for my director using @presentation as a style reference.”
    • When in Chat (via Ask Gemini in Chat) to create a document: "Create a deck outlining Project Zebra with the latest updates"
    • When in Drive (via Ask Gemini in Drive) to create a document: "Create a customer insights deck from project Zebra using my project files including sheets, reports, and emails”
• Conduct deep research: Synthesize complex data scattered across large folders or long threads into a summary report, complete with clear source attributions.
  • Example prompts:
    • When in Docs (via the side panel): “Can you do deep research to see how this blog post compares to other content we have drafted internally and what competitors have published externally?”
• Draft and send emails: Compose detailed emails based on meeting notes or active documents, open as a draft in Gmail or send it directly from the Workspace app you are in.
  • Example prompts:
    • When in Docs (via the side panel): “Send an email to my sales team for their review and feedback"
• Schedule your meetings: Find open times, schedule meetings, or resolve calendar conflicts when communicating with your teams without switching to your calendar.
  • Example prompts:
    • When in Chat (via Ask Gemini in Chat): “Schedule some time for me to discuss next steps with product around launch timelines for our new customer support tool“
• Keep track of your to-dos: Instantly log a reminder or create a to-do list, mapping them straight into Google Tasks.
  • When in Slides (via the side panel): “Based on the presented strategy, can you remind me to follow up with the Marketing team next week to see how the social campaign went?”

Build branded decks without having to leave Docs

Enterprise security and compliance controls are built-in

• Data confidentiality: Your data is not reviewed by humans or used to train Gemini models.
• Granular permissions: Gemini respects the authenticated user’s existing access permissions and sharing policies. If the user cannot access a document, neither can Gemini.
• Human-in-the-Loop controls: For actions involving external communication or calendar commitments, such as sending emails or scheduling meetings, Gemini presents an interactive preview card, allowing users to review, edit, and confirm before execution.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) started on September 2, 2026

Availability

• Business: Standard and Plus
• Enterprise: Standard and Plus
• Consumer: Google AI Pro (with the exception of scheduling functionality) and Google AI Ultra
• Other Editions: Frontline Plus (only for scheduling functionality)
• Education Add-ons: Google AI Pro for Education
• Other Add-ons: AI Expanded Access

Note: Usage of advanced AI features across Workspace apps is subject to usage limits. At launch, this feature will be supported in English only. Support for more languages will be added in the future.

To help organizations elevate their security posture, we are introducing context-aware access (CAA) policies in the Admin console for Gemini Enterprise. Google Workspace administrators can select granular security attributes for Gemini Enterprise access, including device security and location settings that can be applied to personal and managed devices.

For example, an administrator can create a CAA policy that restricts access to Gemini Enterprise from specific geographic regions. Organizations can also reuse their existing policies that apply to Workspace apps by also applying them to Gemini Enterprise.

Getting started

• Admins: Context-Aware Access for Gemini Enterprise can be configured at the organizational unit (OU) or group level. Visit the Help Center to learn more about Context-Aware Access, creating Context-Aware Access levels, and assigning Context-Aware Access levels to apps.
• End users: If enabled by your admin, you can access Gemini Enterprise when authenticating using your Google sign-in. If your organization’s Context-Aware Access settings are not set to allow access, you may see a message letting you know that you cannot use Google sign-in to authenticate with Gemini Enterprise, or you may see remediation messages which will provide some options on how to unblock Gemini Enterprise.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on September 8, 2026, with expected completion by September 15, 2026

Availability

• Enterprise: Enterprise Standard, and Plus
• Education: Education Standard, and Plus
• Other: Frontline Standard and Plus; Enterprise Essentials Plus; Cloud Identity Premium

Note: You will need to have purchased Gemini Enterprise to apply Context-Aware Access policies for your users.

Resources

• Google Workspace Admin Help: Assign Context-Aware Access levels to apps
• Google Workspace Admin Help: Protect your business with Context-Aware Access

How predicting the molecular impact of every possible single-letter DNA variant in the human genome will help accelerate our understanding of biology.

Today, we are introducing AlphaGenome Atlas: a platform containing predictions for the effects of 9 billion single-nucleotide variants — every single-letter change possible — in the human genome. It is the most comprehensive catalogue of how genetic mutations affect molecular biology, and it is available for academic research through an intuitive and free-to-use website portal.

DNA is the language of life. Mastering it is a grand challenge that could transform our ability to understand biology and treat disease. But progress has been limited by a fundamental problem: interpreting how genetic variations impact biology at a molecular level. With roughly 9 billion possible single-letter mutations in the human genome, testing each one in the lab is practically impossible.

Google DeepMind has already made progress on this challenge with AlphaGenome, an artificial intelligence (AI) model that can predict how genetic variants impact biological processes. AlphaGenome is helpful for analyzing specific variants and has found widespread use in research, but we wanted to show researchers a big-picture view of variants across the entire genome.

By precomputing AlphaGenome’s predictions at scale, we have created an easily accessible resource that vastly expands the model's reach. Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome.

To help scientists quickly find the most impactful genetic changes, we are also releasing the AlphaGenome Variant Impact (AVI) score. The AVI combines the strengths of AlphaGenome and AlphaMissense — our model for predicting the impact of protein-altering DNA variants — condensing both models’ predictions into a single number. Now, researchers can rapidly rank variants and interpret their molecular effects at the same time.

Our trusted external collaborators have already used AlphaGenome Atlas to identify and experimentally verify key variants in unsolved rare disease research and find rare variants associated with common traits.

AlphaGenome Atlas is available today through an intuitive website portal, our AlphaGenome API, and as a skill in Google Antigravity.

AlphaGenome Atlas is a massive 1-petabyte dataset, more than 30 times larger than the AlphaFold Database. When we expanded the AlphaFold Database in 2022, we grew the 3D structure information available from around 190K experimental structures to more than 200M structure predictions — covering nearly all catalogued proteins known to science. The database provided a portal that researchers with no coding experience could use, providing intuitive visualizations and making it easier to do large-scale protein structure analysis. It quickly became a crucial resource that drove discoveries across the life sciences and continues to accelerate researchers’ important work in countless fields.

In building AlphaGenome Atlas, we also aspire to make predictions more accessible and give scientists an intuitive way to explore a vast dataset.

AlphaGenome Atlas provides several powerful, interconnected resources, allowing researchers to link variants directly to the functional DNA sequences they disrupt.

• Molecular effect predictions Atlas contains thousands of molecular effect predictions for each variant, across multiple important aspects of gene regulation, spanning hundreds of human and mouse cell types and tissues. This serves as the starting point for further resources.
• AVI score A single number describing the impact for each genetic variant.
• AVI feature attributions Each AVI score is also linked to distinct biological features driving it, such as the aspects of gene regulation predicted by AlphaGenome or the protein impact score from AlphaMissense.
• DNA sequence motifs A comprehensive collection of over 2,500 recurrent DNA sequences — the "words" of the genome — and their locations.

Together, these resources support researchers for a wide range of genetic research tasks, from rapid variant ranking to deep dives into variant functions.

Extensive community collaboration guided the design of AlphaGenome Atlas. The AVI score helps researchers rapidly score and rank variants based on their potential impact. Crucially, it works for both coding regions (the 2% of the genome that codes for proteins) and non-coding regions (the remaining 98%), which orchestrates gene activity and houses most trait-associated variants.

Our testing shows that the AVI score provides best-in-class performance across many variant pathogenicity and rare disease benchmarks. To help interpret these scores, we also calculated AVI feature attributions that highlight which molecular processes — like RNA splicing or gene expression — are predicted to be most disrupted by each variant.

Overview of the AlphaGenome Atlas. (1) Precomputed effects are generated genome-wide for over 9 billion single-nucleotide variants. (2) From this, an allelic-resolution AlphaGenome Variant Impact (AVI) score is derived for each variant. To facilitate variant interpretation, AlphaGenome Atlas then decomposes the AVI score into additive feature contributions across interpretable categories such as chromatin accessibility, splicing, and conservation. (3) The precomputed variant effects, AVI score and the AVI feature attributions are linked, together with a compendium of genome-wide de novo motifs, which enables high-resolution mechanistic insights into variant function.

Real-world impact: From rare diseases to population genetics and molecular biology

AlphaGenome Atlas provides a high-resolution, global view of the genome. These large-scale predictions become most useful when applied to targeted research questions. By translating this data into actionable biological insights, our academic partners are already uncovering links between genetic variation and disease.

Understanding unsolved rare diseases.

A major hurdle in understanding rare diseases is the daunting task of pinpointing the few causal variants hidden among thousands of candidates. In collaboration with the GREGoR Consortium, researchers applied the AVI score to prioritize these needle-in-a-haystack genetic variants for unsolved rare disease research. When Laura Covill and Anne O’Donnell-Luria from the Broad Institute and their colleagues used the AVI score to prioritize variants, driving a rare disease, that were overlooked in previous research, the team discovered a variant affecting a gene called DNM1, which is strongly linked to epileptic encephalopathy.

Crucially, the AlphaGenome predictions underlying the AVI score showed exactly how the variant functioned: it created an incorrect splice site (a mistake in the cell’s genetic instructions) that led to an abnormal extension of the resulting protein. Experimental screens validated the research prediction and found nearby variants with similar effects, showing that Atlas is a powerful tool for understanding impactful genomic variation.

Mapping rare variants associated with protein levels and complex traits.

Moving beyond individual rare disease research, AlphaGenome Atlas can help uncover the genetic architecture of common traits in the general population. Identifying which rare, non-coding variants are associated with a specific trait or disease is notoriously difficult because the sheer volume of harmless genetic changes creates a statistical 'background noise'.

To test how AlphaGenome Atlas can improve our ability to find non-coding variants affecting human traits, Gareth Hawkes, a Medical Research Council fellow at the University of Exeter, applied AlphaGenome Atlas to whole-genome data from over 54,000 UK Biobank participants, which made these elusive signals more obvious. By grouping rare variants based on their predicted molecular effects, Hawkes uncovered 22% more non-coding genetic associations, which would otherwise have not been detectable in the statistical noise. This let Hawkes pinpoint specific regulatory variants driving the abundance of critical proteins circulating in the human body, including PLA2G7 (linked to aging) and EGLN1 (a vital cellular oxygen sensor).

Taking this approach even further, Hawkes used AlphaGenome Atlas to look at how hundreds of millions of non-coding variants in the UK Biobank might be linked to body mass index. By focusing on the 1% of non-coding variants which Atlas predicts to be most impactful, he identified 19 genetic regions, which could help direct the next stage of targeted research into this trait.

Identifying the regulatory ‘words’ of the genome.

Atlas can also be used to identify which recurring short sequences, or motifs, are driving different molecular processes in different cell types for different genes. These motifs can provide key clues, such as locating binding sites of transcription factors (proteins that turn genes on or off) and providing additional interpretation of non-coding variants. Julia Zeitlinger and Melanie Weilert at the Stowers Institute for Medical Research used this resource, for example, to categorize which transcription factors only affect the accessibility of DNA versus which ones are also able to turn genes on and off.

Accelerating genomic discovery

With AlphaGenome Atlas we are creating new layers of information that will help further our understanding of the human genetic code. We hope that this will be a valuable resource for scientists, but we also view it as a baseline rather than an endpoint. As our AI models like AlphaGenome improve, our maps of the entire human genome will become increasingly comprehensive and precise.

AlphaGenome Atlas is powerful in isolation, but it also represents a step towards our vision of a broad, unified solution for biologists. Its resources can be integrated into our broader agentic systems, like Google Antigravity, to help enhance end-to-end scientific workflows.

It is also important that AlphaGenome Atlas’ scientific knowledge is widely available, so we have made it accessible for non-commercial use through our website from today, as well as for commercial use on Google Cloud soon. (The AlphaGenome base model is already available for academic use on GitHub and via the AlphaGenome API, and also is available for commercial use on Cloud via Model Garden).

Together, these tools will enable researchers and industry partners to accelerate the pace of biological discovery: finding novel therapeutic targets, better understanding genetic disorders, and driving the next wave of targeted experimental validation.

The information provided by AlphaGenome Atlas is not intended to be a substitute for professional medical advice, diagnosis, or treatment, and does not constitute medical or other professional advice. AlphaGenome has not been validated for, and is not approved for, any clinical use.

Lyria 3.5, our best-sounding music generation model, is now available in the Gemini app and the Gemini API. Lyria 3.5 brings more expressive vocals and richer musical arrangements, allowing you to craft tracks with higher fidelity.

In the Gemini app, you can now:

• Easily select or describe your genre and choose between vocal or instrumental styles
• Use our new templates to jumpstart your creativity for anything from background music to custom birthday tracks
• Plus, you’ll now have the flexibility to choose short or longer tracks

Whether you need a custom backing track for a video, a unique brand jingle, or just a personalized ringtone, it’s easier than ever to bring your idea to life.

Lyria 3.5 is available to all users globally on the web and in the mobile app. It’s also available for artists and AI creatives in Google Flow Music, and for developers and technologists through Google AI Studio, and Google Vids.

Our flagship AI weather forecasting model now includes real-time satellite data, hourly refreshes, higher resolution, precise precipitation forecasting, and clean energy variables. It’s now integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud.

The WeatherNext team

Rapid weather prediction at unprecedented resolution

Every day, the weather influences billions of decisions. Some are as simple as grabbing an umbrella before heading out the door, but others are far more consequential. Wind, rain, and extreme weather events, like heatwaves and droughts, have cascading impacts across agriculture, global supply chains, clean energy production, and national economies.

In recent years, AI has revolutionized weather forecasting, using historical records to make faster and more accurate predictions than traditional methods. Yet predicting highly local and rapidly changing weather has remained a challenge. Previous models often lacked sufficient spatial resolution, and struggled to incorporate real-time weather data from sources like satellites.

Today, Google DeepMind and Google Research are introducing WeatherNext 3, the most advanced and accurate global weather model to date, according to independent live evaluations by Brightband. Our model learns directly from real-time observations, enabling it to provide timely and more localized predictions for the weather events that impact people the most. By using raw satellite data to produce a forecast every hour in high resolution, our model makes reliable forecasts accessible across Google products worldwide.

A forecast's utility often comes down to detail and how finely it resolves both time and space. WeatherNext 3 generates hourly forecasts at multiple spatial resolutions, maintaining physical consistency from broad global wind patterns all the way down to local topography.

With WeatherNext 3, we can visualize key surface variables — like temperature and moisture — at a 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables, like wind speed, at 25 kilometers. Overall, this provides a global weather picture roughly five times sharper than our previous model, WeatherNext 2, which produced forecasts on a 25-kilometer grid in 6-hour increments.

Real-world data at continuous global scale

WeatherNext 3's biggest leap forward is what it learns from. Most AI weather models, including WeatherNext 2, are trained on data from numerical weather prediction (NWP) models. Although useful, NWP models are complex, supercomputer-driven physics simulations that carry a six-hour data lag. This lag can lead to biases for fast-changing variables like rain or surface temperature.

By ingesting a mosaic of live, global geostationary satellite data, our new model gains a rich, continuously updating view of the atmosphere. This allows the model to generate a new forecast every hour, each one grounded in the most recent satellite observations available, at up to 5-kilometer resolution.

This is important because critical weather develops fast. When storms, fronts, or precipitation systems materialize suddenly, our rapid update cycle and higher resolution provides earlier, more detailed insights needed to help drive an effective response.

Some variables, like temperature and humidity, can fluctuate dramatically over just a few kilometers, which is particularly relevant for communities near coastlines, valleys, or mountain ranges. Traditional models struggle here because they train on representations of the atmosphere that lack detail and miss extreme local variations.

To address this, WeatherNext 3 instead trains directly on sparse weather station observation data. This allows us to make global forecasts on a 5-kilometer grid that account for regional details like topography.

This breakthrough is particularly vital for regions across Latin America, Africa, and Asia-Pacific that have historically been underserved by high-resolution forecasting due to the immense supercomputing costs of traditional regional models. It brings localized, high-fidelity forecasting to billions of people and local businesses in these areas.

Beyond improved resolution and forecast frequency, our model introduces predictions specifically engineered for renewable energy production. The model forecasts 100-meter wind speeds (roughly at turbine-height) for precise wind-energy output, alongside high-resolution cloud cover and sun radiation levels to help solar farms estimate how much light they will receive on the ground.

This data is crucial for global clean energy planning, allowing grid operators and renewables developers to accurately predict how much power their clean energy assets will generate and match it with consumer demand.

Precipitation forecasting at breakthrough accuracy

Global weather models notoriously struggle to accurately predict precipitation. Rain and snow systems are driven by fast-moving cloud processes on tiny scales that are hard to model accurately using traditional physics-based simulations. Consequently, AI forecasts often produce blurry estimates or miss the boundaries of severe storms entirely.

To solve this, we train our model on two exceptionally high-quality sources of precipitation data: NASA’s satellite-based Integrated Multi-satellite Retrievals for GPM (IMERG) and our own global precipitation reanalysis based on satellite radar.

The result is a significant leap in precipitation forecasting accuracy. In medium-range global forecasts, evaluations against baselines show a Continuous Ranked Probability Score (CRPS) improvement of up to 60% against IMERG, 30% for MRMS, and 10% against rain gauge measurements for early lead times.

Research applied across the ecosystem

Our primary goal is to advance weather intelligence to make it universally useful — whether for an emergency responder tracking sudden wind shifts, an air traffic controller planning flight paths, or a farmer managing crops.

To bring these breakthroughs out of the lab and into the real world, we’re integrating WeatherNext 3 across Google’s core ecosystem and beyond:

• High-resolution forecast data: We’re making global weather predictions, updated hourly and ready to integrate into your workflows with no model setup required. This enables researchers, developers and businesses to query the data in BigQuery and Earth Engine, or bulk-download from Google Cloud Storage.
• Available globally: WeatherNext 3 will begin powering weather experiences within Google Search, Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today. The update dramatically improves longer term forecasts. When planning a day or more ahead, people will see up to 50% more accurate precipitation forecasts — with the greatest improvements in regions where forecasts have historically been less reliable. So if you’re packing for a weekend trip or deciding the best day for an outdoor activity, you’ll now get more accurate predictions to help you plan.

The atmosphere will always retain a degree of unpredictability. However, by training on real-world observations and bypassing traditional modeling constraints, WeatherNext 3 brings us closer to a future where forecasts truly match what is happening on the ground.

To learn more about geospatial platforms and AI work at Google, check out Google Earth Engine, AlphaEarth Foundations, and Earth AI.

Disclaimer: For official weather forecasts, severe weather warnings, and public safety advisories, please refer to your local meteorological agency or national weather service.

Learn more about WeatherNext 3

• Read our paper
• Build with WeatherNext 3
• Explore Weather Lab to see WeatherNext 3 visualized in real-time
• See where WeatherNext 3 ranks on independent live leaderboards from Brightband.

Earlier this year, we introduced the ability for Workspace users to set persistent custom instructions for Gemini in Google Docs. We're now expanding support for these custom instructions to additional Gemini in Workspace surfaces, specifically:

• Ask Gemini in Drive
• Ask Gemini in Chat
• Gemini side panel in Slides, Sheets, and Gmail

These instructions help personalize your interactions with Gemini and ensure that Gemini adapts to your style, tone, and formatting preferences without needing to repeat them in every conversation, ultimately saving you time and ensuring consistency.

With this update, users can build a set of custom instructions that Gemini respects across these Gemini surfaces. The update ensures that users have a consistent personalization experience across the platform based on their individual needs or preferences.

You can declare preferences in any Gemini in Workspace Surface

All saved instruction can be viewed and managed in the Personalization Setting tab

These instructions are then used to personalize Gemini’s responses across Workspace

Getting started

• Admins: There is no admin control for this feature.
• End users: Get started by opening the side panel, Ask Gemini in Drive, or Ask Gemini in Chat. You can then prompt Gemini to store a specific instruction. You can also access the ‘Your Instructions for Gemini In Workspace” Menu by selecting the hamburger menu > Settings > Personalization. Visit the Help Center to learn more about customizing Gemini in Workspace's responses with your instructions.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on September 2, 2026

Availability

• Available to all Google Workspace customers with access to Ask Gemini in Drive, Ask Gemini in Chat, and/or the Gemini side panel in Gmail, Sheets, and Slides. See more details on feature availability here.

Resources

• Google Help: Customize Gemini in Workspace's responses with your instructions

Today, we’re launching our Fairwind Program, a limited access program for governments and trusted partners to use our most advanced cyber defense capabilities.

Defenders wanting to use advanced AI have faced a difficult dilemma: adopt enormous frontier models that could be expensive to deploy and difficult to control across enterprise codebases, or turn to smaller open-weight models that might struggle with complex vulnerability remediation and require teams to build their own tooling and infrastructure from scratch. Until now.

Today, we’re launching our Fairwind Program to bring the best of Google’s AI and cyber defense capabilities to a trusted group of Google Cloud customers, government agencies, and cybersecurity partners, to help them proactively solve cyber risks at scale. As a first step, the Fairwind Program will give defenders access to powerful and advanced Gemini models to help them autonomously find and fix vulnerabilities, protecting critical infrastructure, public services, and national security.

Finding and autonomously fixing vulnerabilities

The Fairwind Program offerings bring together our most advanced cyber model, Gemini 3.8 Flash Cyber, with our CodeMender harness, to help defenders find, verify, and fix vulnerabilities at agentic scale. Spotting weaknesses creates awareness and fear; autonomously finding and fixing vulnerabilities delivers security.

CodeMender with Gemini 3.8 Flash Cyber delivers the specialized reasoning to write and validate code fixes, at a fraction of the operating cost of traditional frontier models. Instead of taking weeks to manually fix vulnerabilities, defenders can now generate verified, deployment-ready patches in minutes — within an organization’s secure cloud environment.

Scaling frontline defense

Providing early access to these powerful cyber capabilities gives trusted defenders a vital adaptation window to harden their systems before bad actors have a chance to exploit new capabilities. We’re staging initial access to government and enterprise partners most critical to society’s resilience:

• Governments and national cyber authorities: Hardening public-sector networks and citizen services against targeted intrusions.
• Critical infrastructure operators: Protecting essential services across healthcare, telecommunications, energy, and financial networks from operational disruption.
• Core technology platforms: Securing widespread software foundations to uplift digital security for millions of downstream users at once.

To ensure these powerful AI capabilities are used responsibly, participating organizations agree to strict operational standards, including limiting access to employees within their internal cybersecurity, incident response, or penetration testing teams and deploying protections like multi-factor authentication.

We have more than 650 participating partners globally, including:

What our Fairwind Program partners are saying

Trusted defenders and industry leaders are already putting Gemini 3.8 Flash Cyber into practice:

The Fairwind Program will evolve alongside our partners and users' needs. We will adapt our product offerings and expand partner access, collaborating closely with industry, governments, and open-weight community leaders to strike the right balance between open access and robust security.

While we are prioritizing Gemini 3.8 Flash Cyber access for customers in the Fairwind Program, any Google Cloud customer can proactively secure their code by using CodeMender with publicly available models hosted on Gemini Enterprise Agent Platform, in combination with industry-leading solutions offered through AI Threat Defense.

Making an ecosystem-scale impact on cyber defense

Years of Google’s pioneering zero-trust architecture, advanced AI defenses, and built-in security allow us to protect billions of accounts daily – keeping more people and organizations safe online than anyone else. The Fairwind Program builds on this experience and is part of our broader commitment to global cyber resilience across the entire digital ecosystem, including helping to fortify grassroots cyber defense.

Through Google.org, our latest commitment brings our total cybersecurity funding to more than $100 million globally. We’re pleased to release our 2026 Google.org US Cybersecurity Impact Report, which details $36 million in funding for 35 cyber clinics to date, providing free, hands-on security support to over 1,250 hospitals, public school districts, and municipal utilities in the U.S.

Providing a security advantage

The defender’s edge comes from shrinking the time between detecting a flaw and patching it. Through Google’s Fairwind Program, government and enterprise partners gain autonomous tools to repair systems faster and at scale, keeping them one step ahead of agentic-speed threats.

Our newest Gemini models deliver next-generation intelligence for agentic workflows and cybersecurity.

Building on the momentum of 3.7 Flash from three weeks ago and marking our third Flash release in only six weeks, today we’re introducing Gemini 3.8, our best reasoning & coding model yet, at the same speed and low cost of 3.7. Gemini 3.8 introduces 2 variants:

• Gemini 3.8 Flash: our most intelligent workhorse model, delivering significant improvements from 3.7 Flash across software engineering, agentic tasks, and critical, multi-step reasoning in specialized domains. It is available at the same introductory price1 as 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.
• Gemini 3.8 Flash Cyber: our most capable cybersecurity model with frontier-level performance in vulnerability detection and automated patching, available to trusted defenders through our new Fairwind Program.

While tailored for different deployment environments, both of today's releases are powered by the same foundational intelligence, and further accelerated by long-running agentic loops designed to recursively evaluate and refine the underlying models. The significant coding and reasoning gains across this shared core were driven by a number of innovations, including rigorous training in the highly demanding domain of cybersecurity.

Gemini 3.8 Flash: built for long-horizon coding and autonomous agents

Gemini 3.8 Flash delivers substantial gains from 3.7 Flash, often approaching the performance of higher-cost frontier models.

On DeepSWE v1.1 (Long-Horizon Software Engineering) 3.8 Flash outperforms most larger frontier models in autonomously solving complex engineering problems end to end, only at a fraction of the cost.

Additionally, 3.8 Flash exhibits the dependability required for critical enterprise autonomy, across specialized knowledge domains.

In quantitative and professional fields that require advanced analysis and reporting, 3.8 Flash outperforms 3.7 Flash and other frontier models in benchmarks like Vals Finance Agent V2 and Harvey's Legal Agent Benchmark. 3.8 Flash also achieves a 54.9% on HLE-Verified, demonstrating its ability to handle multi-step reasoning across STEM, humanities, and professional fields.

These performance gains stem from a core design choice: 3.8 Flash works harder. On complex tasks, it exhibits greater diligence — executing extra reasoning steps, and calling tools iteratively. At times, the model might use more tokens to maximize performance, especially at higher effort levels.

For applications where compute efficiency is the primary constraint, developers can utilize lower effort levels to minimize token overhead or continue to rely on Gemini 3.7 Flash, which remains fully supported for efficiency-first workloads.

Gemini 3.8 Flash Cyber: expert cyber performance

Gemini 3.8 Flash Cyber, available to a set of trusted defenders via the Fairwind Program, provides a decisive advantage in today’s complex cybersecurity landscape, with the Flash speed and cost that enables quick iteration.

Autonomous vulnerability discovery

On the standard industry benchmark for finding vulnerabilities, CyberGym, Gemini 3.8 Flash Cyber demonstrates frontier-level performance in autonomous vulnerability discovery. It surpasses both 3.5 Flash Cyber as well as significantly larger frontier models.

To better capture real-world defensive needs which are not limited to just C/C++ codebases like in CyberGym, we also evaluated Gemini 3.8 Flash Cyber against a comprehensive internal benchmark in which the model has to discover a wide range of vulnerabilities across complex codebases spanning 20 programming languages. Here, the model showcases an impressive leap over our previous models and reaches a success rate exceeding 70%.

Automated patching

With Gemini 3.8 Flash Cyber, we focused specifically on equipping defenders with expert capabilities that give them an advantage over attackers. This is why we have invested in vulnerability fixing from the start, and prioritized it over offensive capabilities like exploitation.

CWE-Bench, run by Collinear, is a challenging external benchmark for patching capabilities. On this benchmark, Gemini 3.8 Flash Cyber is on the Pareto frontier: with a pass@1 of 47.2% compared to a leading frontier model at 47.8%, yet offered at a significantly lower cost.

Real-world impact: securing Google’s code

We’re already using Gemini 3.8 Flash Cyber to secure code across Google. For example:

• The Chrome Security team found that 3.8 Flash Cyber produced 2.6 times more correct patches to vulnerabilities in Chrome than the best commercial models that are much larger.
• Wiz found that Gemini 3.8 Flash Cyber achieves +7.5-9.7% higher recall on their internal penetration testing benchmark for a 2.3-5.2x lower cost compared to other leading frontier models.
• Google’s Cloud Vulnerability Research team leveraged the 3.8 Flash Cyber model to find a critical foundational vulnerability in less than 2 hours, a vulnerability for which research and discovery usually takes months.

What our Fairwind Program partners are saying

[Quotes from partners shown as images]

Built with safety in mind

3.8 Flash ships with safeguards against misuse in the domains of Chemical, Biological, Radiological, and Nuclear (CBRN) and cyber offense, while enabling beneficial use cases, as per our Frontier Safety Framework. 3.8 Flash Cyber ships with a more permissive set of mitigations for cybersecurity, and as such, is only available to trusted defenders who require a more comprehensive set of cyber capabilities.

Gemini 3.8 models have also made a significant leap in prompt injection robustness as measured by Gray Swan, protecting Gemini model users from prompt-injection related malicious attacks.

Gemini 3.8 Flash and Cyber: get started today

• Developers: Build with 3.8 Flash and explore agent-first workflows in Google Antigravity or start building today in the Gemini API via Google AI Studio and Android Studio, or generate UIs in Stitch. Get started with our developer docs.
• Enterprises: Access 3.8 Flash in Gemini Enterprise.
• Consumers: 3.8 Flash is available to Google AI Pro and Ultra subscribers across the Gemini app, AI Mode in Google Search and Gemini in Google Sheets.
• Cyber: Through our new Fairwind Program, we’re providing trusted government authorities, as well as critical infrastructure operators and software maintainers with prioritized access to Gemini 3.8 Flash Cyber. Apply for access.

Here’s a recap of some of our biggest AI updates from August, including the launch of Gemini 3.7 Flash, Gemini 3.5 Transcribe, and the all-new Pixel 11 series of devices.

For more than 20 years, we’ve invested in machine learning and AI research, tools, and infrastructure to build products that make everyday life better for more people. Teams across Google are working on ways to unlock AI’s benefits in fields as wide-ranging as healthcare, crisis response, and education. To keep you posted on our progress, we're doing a regular roundup of Google's most recent AI news.

Here’s a look back at some of our AI announcements from August.

In August, we continued advancing AI responsibly — making it faster, more accessible, and truly practical for everyone, from software developers and students to creatives and scientists. We’re bringing intelligence directly to where people work and live, with powerful new hardware designed for Gemini in the Pixel 11 series, cost-efficient developer models like Gemini 3.7 Flash, the rollout of Gemini in Chrome on Android, and hands-free voice tools across Google Workspace and Gemini Live. As the Gemini app officially crossed 1 billion monthly users, we also expanded our creative and scientific footprint, introducing studio-quality video and music generation alongside open-source AI models that predict weather patterns and tackle climate challenges. Overall, August marked a shift toward AI that isn't just powerful in theory, but useful in everyday reality.

Build better agents at a lower cost with Gemini 3.7 Flash.

We released Gemini 3.7 Flash as our most intelligent workhorse model yet for coding and agents. It arrived just three weeks after our launch of 3.6 Flash, delivering substantial improvements across software engineering, knowledge work, and web development workflows — with an introductory price of half the original 3.6 Flash cost per million tokens.

Try the new Pixel 11 series, designed for Gemini Intelligence.

At Made by Google 2026, we unveiled Pixel 11, Pixel 11 Pro, Pixel 11 Pro XL, and Pixel 11 Pro Fold. These devices come with major camera upgrades, enhanced durability, and our fastest, most powerful chip, Google Tensor G6, that runs the latest Gemini Nano model. They’re also designed for Gemini Intelligence to deliver time-saving, personal help. See all the announcements from Made by Google 2026.

Start the semester with one year of Gemini, on us.

We’re offering one year of a Google AI plan free of charge for eligible college students around the world — plus new and enhanced study tools — so students can make the most of this school year. We’ve also got tools to help teachers and students as they head back to school, including a dedicated student hub, new teacher-led tools, and SAT prep in Gemini.

Level up your learning with Search.

To help you start the semester with confidence, we've added new AI-powered learning features in Search — all built to be safe by design. With these updates, Search can help you grasp complex concepts through interactive visuals, generate practice quizzes for exams like the SAT, ACT, GRE, and LSAT, learn step-by-step with Lens, and stay organized with notebooks.

Get more intelligent transcription with Gemini 3.5 Transcribe.

Our latest speech-to-text model delivers precise, intelligent real-time transcription for developer workflows like voice agents, live captioning, and post-call analytics. Unlike conventional models that struggle with noise and jargon, Gemini 3.5 Transcribe converts raw audio directly into accurate, polished, context-aware understanding.

Get more done with new productivity features in Gemini Live.

Gemini Live is moving beyond conversation to handle complex tasks on your behalf. With new features like Personal Intelligence, Daily Brief, Spark, and hands-free inbox management, you can easily talk through your day and delegate your to-dos without missing a beat.

See how more than 1 billion people are using the Gemini app every month.

The Gemini app officially surpassed 1 billion monthly users, making it the fastest-growing product in Google’s history. To mark the milestone, we shared some usage insights, such as: 63% percent of users now talk directly to Gemini, including more “voice only” users — with busy parents 43% more likely to use it for everyday tasks. Gemini now generates 150 million+ images every day, and small businesses are power users, relying on Gemini's all-in-one image, video, and audio creation to craft marketing materials.

Generate videos with more control using Gemini Omni 1.1 Flash.

We introduced Gemini Omni 1.1 Flash to bring people even more precision and control for generating videos. The new capabilities deliver studio-quality video production — including scene extension, first-and-last-frame interpolation, crisp 4K upscaling, and faster prototyping. Omni 1.1 Flash is now available in Google Flow, Google AI Studio, the Gemini Enterprise Agent Platform, and the Gemini app.

Explore how Gemma is offline everywhere from outer space to underwater.

We released Gemma to help developers build responsible, innovative AI applications anywhere. Over one billion downloads later, Gemma supports environments from phones and edge infrastructure to space. You can explore some of the ways people are using it — from researching interspecies communication to driving medical breakthroughs — and share, discover, and collaborate in our new community repository.

Learn about Operation Blue Skies, a project to reduce aviation climate impact with AI.

Our AI-powered forecasts already help flight crews and air traffic controllers adjust routes to avoid forming contrails — all within normal flight operations. Now, we’re partnering with the UK Government and aviation leaders to expand this technology across the North Atlantic, helping airlines reduce aviation’s climate impact on a global scale.

See how WeatherNext 2 demonstrated a massive leap forward in predicting cyclones.

In a Nature paper, our researchers showed that WeatherNext 2 predicts cyclone track, intensity, and wind structure with state-of-the-art accuracy — delivering a decade of meteorological progress in one model. Now, we’re open-sourcing WeatherNext 2 to the research community to help build global climate resilience.

Our new agentic feature for video analysis cuts token consumption by up to 88%, reduces costs by up to 66%, and boosts quality by up to 7%.

Today, we’re launching agentic video understanding across our latest models: Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite. This new capability improves accuracy while dramatically reducing token usage and costs for video analysis. Similar to agentic vision, which combines code execution with Gemini models’ native image understanding, agentic video understanding uses Gemini’s native video tools to improve performance and unlock new capabilities for video processing like sub-second moment retrieval, more accurate anomaly detection, precise counting and more.

The feature is available today for video uploads and YouTube videos via the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform.

Benchmarks

Unlike current ‘static’ processing, where the model ingests the video at a fixed frames-per-second rate (default 1 FPS, adjustable via API), agentic video understanding pairs the model’s core reasoning with native video tools to dynamically search, scan, and inspect target video segments across visual frames, audio, and transcripts. Across standard video analysis benchmarks, Gemini models with agentic video understanding reduce analysis costs by up to 66% and token consumption by up to 88%, while improving accuracy by up to 7%.

These efficiency gains are especially pronounced on long-form video (from 10-minute how-to guides to 90-minute lectures and multi-hour recordings), where static processing forces developers to choose between high token costs or techniques that drop critical details.

Activating agentic video understanding drops token consumption by up to 88% and boosts accuracy by up to 7% with Gemini 3.7 Flash.

While these gains span all three supported models, Gemini 3.7 Flash with agentic understanding offers the best possible quality overall and the best combination of quality and cost efficiency, putting it at the accuracy-to-cost pareto frontier among tested models for video understanding.

Using agentic video understanding places Gemini 3.7 Flash at the accuracy-to-cost pareto frontier for video analysis.

How it works

Instead of static processing where the model ingests media streams at a fixed frame rate, agentic video understanding enables Gemini to take an active, goal-directed role in determining what to watch, at what speed, and through which modality (frames, audio, or transcript), fetching only the moments and signals needed. While developers could previously do this manually, with agentic video understanding, Gemini can accomplish it through an agentic loop, invoking an internal tool to load the relevant part of the video file, significantly reducing development overheads.

Capabilities and use cases

Agentic video understanding transforms how developers can process long-form video content across a variety of demanding applications.

• Sub-second moment retrieval: Pinpoint split-second state changes and tight cut boundaries that are easily missed at 1 FPS, making precise automated video editing possible.
• Long-form needle-in-a-haystack search: Answer complex queries across multi-hour videos without consuming millions of tokens.
• Anomaly detection: Resample interesting time windows at higher FPS to inspect rapid motion and subtle visual artifacts.
• Counting action & object: Accurately track repeated physical movements and distinct objects over time.

Real-world results

Many of our early access partners saw strong performance while testing with agentic video understanding. Here’s what they have to say:

Getting started

Agentic video understanding is available via the Gemini API in Google AI Studio and Gemini Enterprise Agent Platform, launching across Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite. It uses standard Gemini API token pricing with no additional feature fee.

To enable it, simply set processing to "agentic" in the API configuration. Read our developer guide to get more insights into the feature and how to get started.

We are also bringing the efficiency and quality improvements of agentic video understanding to billions of users across Google products. The feature will roll out to all users in the Gemini app across Flash and Flash-Lite models soon. And in the coming months, agentic video understanding will also power YouTube's ‘Ask YouTube’ feature on the video watch page, leveraging Gemini to deliver higher-quality answers grounded in the visuals.

Acknowledgement for their contribution to this work:
Sergi Caelles, Filip Pavetić, Ahmet Iscen, Suhas Yogin, and the Agentic Vision team.

Building trust in proprietary model benchmarks using cryptographically secure environments

Imagine a student is set to take a high-stakes exam. If they accidentally peek at the test questions in advance, achieving a perfect score is influenced by this knowledge, making it a meaningless accomplishment. To truly measure what they know, they must have no visibility of the test questions until it's time to take the exam. That is the exact challenge the industry faces when evaluating advanced AI models. If a model has already seen the test questions - a problem known as benchmark contamination - the results can only be trusted to an extent.

Today, we’re introducing the world’s first double-blind evaluation of a proprietary, frontier class AI model, which keeps external evaluations confined to a cryptographic “box” where they can’t be used by models later to optimize performance ahead of testing. We're partnering with the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons, to test a Gemini Flash Lite model against confidential benchmarks in a privacy-preserving environment, increasing evaluation integrity.

At Google, we assess our AI systems using a broad spectrum of evaluations throughout model development and deployment, but we don’t rely on internal testing alone. To identify potential blindspots, we work with a diverse group of external partners, including specialized research labs, civil society and national AI Safety and Security Institutes (AISIs), using their unique expertise to stress-test our models.

As AI models become more capable, ensuring the model has not seen the test questions or prompts in advance is critical, as this can skew the results. Policymakers, researchers, and enterprises need to trust that AI benchmarks accurately reflect a model's true capabilities and safety, but if models are able to “peek” at the evaluation questions in advance, it can artificially inflate scores and undermine this trust.

Although zero-logging protocols and rigorous contractual safeguards have long kept external test prompts confidential, incorporating technical and cryptographic safeguards marks a major step forward in secure model evaluation.

How double-blind evaluations work

Historically, high-stakes external evaluations required a tradeoff. Either evaluators handed over their testing prompts (risking the model provider seeing the test questions in advance), or the model provider handed over their model weights (risking their intellectual property).

Double-blind evaluations eliminate this compromise. By using Confidential Space within Google Cloud’s Confidential Computing portfolio, we can cryptographically verify that both the external evaluation data and the proprietary model remain private to their respective owners. The evaluator cannot see the Gemini model weights, and Google cannot see the evaluator’s test prompts.

A novel approach to building trust in model evaluations

This cryptographic evidence helps prevent benchmark contamination and protects sensitive data. As models become more capable this becomes particularly important for highly sensitive evaluations, such as those used for cybersecurity or by government bodies. Double-blind evaluations unlock the ability for independent organizations to rigorously test advanced models without compromising data sovereignty or security.

We hope this pilot establishes a new frontier for model oversight, helping the broader industry build safer, more reliable, and widely trusted AI systems. To learn more about our methodology and findings, read our technical report.

Extend scenes for longer storytelling

Scene extension allows you to take an existing video and continue generating footage seamlessly from where it left off.
With Omni 1.1, the model can now analyze up to 10 seconds of prior context — a leap from previous models that only referenced the final second. The result is improved visual consistency and narrative adherence, letting you build longer stories or branch into new creative directions. You can extend videos in 10-second increments up to a total cumulative length of 40 seconds.

Here’s how you can extend your scene with the Gemini API:

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    previous_interaction_id=previous_video_interaction.id,
    input=[
        {
            "type": "text",
            "text": "Continue the scene."
        }
    ],
    response_format={
        "resolution": "360p",
    },
)

Specify first and last frames

Achieve smooth transitions and camera movements by specifying the starting and ending frames of a shot. Omni 1.1 generates continuous video between two keyframes, making it ideal for complex camera orbits, zoom transitions, or seamless looping clips.

Draft videos more efficiently in 360p

Generate lightweight previews in 360p resolution up to 60% faster* and at a third of the cost compared to Omni 1.1’s standard 720p resolution. This is helpful for rapid prototyping, storyboard iteration, and quick rendering in developer platforms.

*Up to 60% faster generation based on system throughput of 360p vs. 720p resolution

Upscale up to 4K resolution

Generate polished, high-resolution 1080p or 4K outputs that are ready for professional production with Omni 1.1.

Add video references in your multimodal input

Reference up to three seconds of video when crafting your scene, allowing you to maintain visual context and character consistency based on video references.

Inspiring concepts for what you can build

Here are a few ideas showing how developers can put these new capabilities into action across custom tools and creative workflows.

See how customers are putting Omni Flash in production

Our customers are already driving real-world production with Gemini Omni Flash via the Agent Platform API. Explore the videos they've created and hear about how they are using the model below.

Build with Gemini Omni 1.1 Flash Today

Omni 1.1 is rolling out across the Google developer ecosystem:

• Start building in Google AI Studio: Try out Omni 1.1 directly in Google AI Studio.
• Build on Gemini Enterprise Agent Platform: Enterprises can build with Omni 1.1 directly via Agent Platform API.
• Explore the developer documentation: Check out the official documentation, the cookbook and prompting guides to learn how to integrate scene extensions, video references, and upscaling into your applications.

Omni 1.1 is also available to all Google AI Plus, Pro and Ultra subscribers globally in Google Flow, starting today. Scene extension is available to all Google AI Plus, Pro and Ultra subscribers globally in the Gemini app.