Jev-AI CLI tool that evaluates text against custom rulesets
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Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet. Major upgrades in intelligence and parallel reasoning make them more intuitive to collaborate with and use to execute complex tasks using your voice.
Tom Ouyang
Principal Engineer
Malini Jaganathan
Member of Technical Staff, on behalf of the Gemini Audio Team
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This content is generated by Google AI. Generative AI is experimental
Updated September 17, 2026
Today, we’re introducing two new models that bring advancements in near real-time reasoning to more effectively enable voice agents and make conversing with AI feel more intuitive and intelligent.
- Gemini 3.8 Live: Built for scale and cost efficiency, combining conversational intelligence with fluid dialogue and visual grounding.
- Gemini 3.8 Live Extended Thinking: Built for high-complexity tasks, with increased intelligence and multi-step reasoning.
For developers and enterprises, these models deliver the building blocks for reliable, production-ready voice agents. They also make speaking with Gemini across the Gemini app, Google Workspace, and Search more fluid and collaborative — helping you tackle complex tasks using just your voice.
Experience more fluid, intelligent conversations
Gemini 3.8 Live Extended Thinking provides enterprise-grade task completion and intelligence, capturing the #1 overall spot on Artificial Analysis’ Speech to Speech Quality Index (82.6), and leads in agentic task completion with 68.6% on τ-Voice and 35.1% on Sierra’s τ-Voice-banking benchmark. It also provides strong reasoning capabilities, scoring 97.7% on Big Bench Audio, while maintaining a highly competitive price point compared to other frontier models.
Gemini 3.8 Live has shown a high preference among users, securing a second place in the Speech Agent Arena. In addition to this performance, it remains highly cost-effective — providing developers and enterprises with a capable and efficient model built for scale.
On ServiceNow’s EVA-Bench, a benchmark for evaluating voice agents, our models push the Pareto Frontier for complex workflows by successfully balancing accuracy with conversational quality.
Note: This was run on the Live API on Gemini Enterprise Agent Platform.
Gemini 3.8 Live processes visual inputs in near real-time, enriching conversations with context for more helpful responses. It automatically detects and transitions between 97 supported languages mid-conversation. It executes tools and API calls in the background while continuing the conversation, so the model can acknowledge requests and keep chatting while tasks finish in the background.
For tasks that require deeper reasoning, 3.8 Live Extended Thinking reasons and speaks simultaneously. It delivers increased intelligence for complex workflows while maintaining an uninterrupted conversational flow — using early verbal cues like “Let me check that…” to acknowledge prompts naturally, and live progress narration to walk users through multi-step background tasks as they progress.
Across Google Workspace, Search, and the Gemini app, our Live models deliver more intuitive, collaborative experiences — especially when tackling your most complex tasks.
Empowering the developer and enterprise voice ecosystem
By using the Gemini Live API, developer platforms such as Agora, Fishjam, LangChain, LiveKit, Pipecat, Vercel, and Vision Agents enable developers to build and deploy high-performance voice-driven interfaces with ease. These platforms manage complex real-time media streaming infrastructure behind the scenes, allowing developers to focus entirely on crafting the user experience.
We’re also partnering with companies like Salesforce, Genspark, and Lumeris who are excited about 3.8 Live and 3.8 Live Extended Thinking, highlighting its impressive latency, fluidity, and tool-calling capabilities.
Ensure transparency with SynthID watermarking
All audio generated by our AI products is watermarked with SynthID. This imperceptible watermark is woven directly into the audio output, ensuring AI-generated content remains detectable to help prevent misinformation. For details on our approach to safety and responsibility, review the model card.
Start using our latest Gemini Audio models:
3.8 Live is rolling out starting today:
- For developers: In the Gemini API and Google AI Studio
- For enterprises: In private preview in Gemini Enterprise and coming soon to Gemini Enterprise for Customer Experience
- For everyone: In Search Live
3.8 Live Extended Thinking is rolling out starting today:
- For developers: In the Gemini API and Google AI Studio
- For enterprises: In private preview in Gemini Enterprise and coming soon to Gemini Enterprise for Customer Experience and Google Workspace business customers
- For everyone: In Gemini Live and for Google AI Pro and Ultra subscribers in Workspace in Docs, and all Google AI subscribers in Gmail and Keep
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AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome
September 8, 2026 Science
AlphaGenome Atlas team
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 (detailed in our paper) 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
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.
Acknowledgements
We are grateful to our research collaborators at University of Exeter, Broad Institute, Boston Children’s Hospital, Stowers Institute for Medical Research, Harvard University, Memorial Sloan Kettering Cancer Center, Center for Genomic Medicine at Massachusetts General Hospital, and the University of Kansas Medical Center.
This work was done thanks to the contributions of Jun Cheng, Kyle R. Taylor, Lauren Nicolaisen, Joshua Pan, Clare Bycroft, Matteo Perino, Tom Ward, Raina W. Thomas, Natasha Latysheva, Gareth Hawkes, Laura E. Covill, Melanie Weilert, Maile J. Hirschmann, Xi Dawn Chen, Robin N Beaumont, V Kartik Chundru, Michael N Weedon, Simon Bourdareau, Hoyin Chu, Dhavi Hariharan, Thais Kagohara, Lucas Tenório, Yosuke Ushigome, Amanda Stafford, Courtney A. Shearer, Barbara Ikica, Ada Fang, Mouad Naciri, Victoria Johnston, Richard Green, Elisa Lai Hong Wong, Vincent Dutordoir, Anne Mottram, Adam Gayoso, Eirini Arvaniti, Guido Novati, Heidi L. Rehm, Fei Chen, Caleb A. Lareau, Caroline F Wright, Anne O’Donnell-Luria, Julia Zeitlinger, Pushmeet Kohli, Žiga Avsec.
We wish to thank our teammates and collaborators for their technical support, feedback, including Kathryn Tunyasuvunakool, Alexander Karollus, Risha Patel, Francesca Pietra, Alisha Eastep, Doga Fadillioglu, Charlie Taylor, Raphael Aboyeji, Uchechi Okereke, Gemma Gibbs, Olufemi Duduyemi, Juan Mateos-Garcia, Mariana Felix, Sahar Abdulrahman, Antonia Mould, Rachael Tremlett, Chang Yun, Salil Deshpande, Anshul Kundaje, Samantha Bryen, Greg Findlay, Phoebe Dace, Kinga Bujakowska, Emma Sherrill, Aubrie Soucy Verran, Boxun Zhao, Tim Yu, Francesca Pietra, Brendah Namugamba, Cassie Gray, Daniel MacArthur, Lingyi Wang, Marc Mansour, Mohamad Hajjari, Mounica Vallurupalli, Philip Montgomery, Phoebe Dace, Roisin Sullivan, Sam Bryen, Teresa Niccoli,
Finally we wish to thank Evie Gray, Adriana Fernandez Lara, Alex Wilkins, Danielle Breen, Mariana Montes, Inês Ayer, Ryan Smith, Ross West and Gaby Pearl for their expertise in communicating this work.
AlphaGenome
AlphaGenome: AI for better understanding the genome
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Introducing WeatherNext 3, our most advanced and accurate global weather AI model
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.
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This content is generated by Google AI. Generative AI is experimental
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.
Rapid weather prediction at unprecedented resolution
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.
Figure 1: The end-to-end WeatherNext 3 system architecture. The model ingests live 1-hour geostationary satellite mosaics alongside traditional historical analysis to feed a single, flexible Functional Generative Network (FGN) mesh transformer. It outputs dense gridded fields, discrete cyclone tracks, and predicts station-level sparse coordinates natively.
Figure 2: Comparison of 2-meter temperature forecasts over the UK. WeatherNext 2 (left) at 25-kilometer (0.25°) resolution vs. WeatherNext 3 (right) at a native 5-kilometer (0.05°) resolution. WeatherNext 3 resolves the intricate local topography, preventing the pixelated, over-smoothed thermal representations seen in older models.
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.
Figure 3: Medium-range probability of precipitation (PoP > 1mm) forecast comparison. WeatherNext 2 (left) at 25-kilometer resolution shows a highly diffused and pixelated precipitation footprint. WeatherNext 3 (middle) at 11-kilometer resolution closely mirrors the actual satellite ground truth (right), accurately capturing the sharp, convective bands of the weather systems.
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.
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Proactive cyber defense for governments and enterprises
Today, we’re launching our Fairwind Program, a limited access program for governments and trusted partners to use our most advanced cyber defense capabilities.
Four Flynn
Vice President – Security and Privacy
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This content is generated by Google AI. Generative AI is experimental
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.
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Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
Our newest Gemini models deliver next-generation intelligence for agentic workflows and cybersecurity.
Raluca Ada Popa
Gemini Security Lead, Google DeepMind
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 and 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 price 1 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
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.
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Introducing agentic video understanding with Gemini
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%.
Rohan Doshi
Senior Product Manager, Google DeepMind
Mario Lučić
Research Director, Google DeepMind
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This content is generated by Google AI. Generative AI is experimental
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.
from google import genai client = genai.Client() interaction = client.interactions.create( model="gemini-3.7-flash", input=[ { "type": "video", "uri": "https://youtu.be/7Z5Vy9JBANs", "processing": "agentic" }, { "type": "text", "text": "What are the 3 most important announcements in this keynote?", }, ], ) print(interaction.output_text)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.
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GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models
At a glance
- The Flash family extends GigaPath and GigaTIME with dramatically improved efficiency, making large-scale pathology research more accessible and practical.
- A distilled pathology foundation model backbone reduces computational requirements without sacrificing performance, enabling repeated analyses across larger patient cohorts.
- These open models support population-scale discovery, helping researchers investigate disease biology, biomarkers, and clinical outcomes across diverse cancer datasets.
GigaPath (opens in new tab) and GigaTIME (opens in new tab) demonstrated how foundation models can support whole-slide analysis and tumor microenvironment modeling from routinely collected pathology data. GigaPath-Flash and GigaTIME-Flash make these capabilities substantially more efficient, enabling researchers to analyze larger cohorts, run more experiments, and move toward population-scale discovery. GigaPath-Flash and GigaTIME-Flash are research models. They are not intended or validated for clinical use, including diagnosis, prognosis, treatment selection, or other patient-care decisions. Performance may vary across datasets, scanners, institutions, populations, and use cases.
The scale opportunity in computational pathology
Histopathology is among the richest and most widely available sources of information in cancer research. Every tissue biopsy produces a whole-slide image that captures cellular morphology at subcellular resolution — and hospitals generate millions of these slides each year. This data contains information relevant to diagnosis, prognosis, treatment selection, and the biology of the tumor microenvironment.
Foundation models have begun to unlock this information at scale. But whole-slide images are large — often exceeding a gigapixel — and applying a foundation model to even a single slide requires processing thousands of image tiles. When a research question involves tens of thousands of patients, the computational cost grows quickly. And population-scale discovery is not a single model run: it requires repeated cycles of feature extraction, statistical analysis, hypothesis testing, and validation across patient subgroups, biomarkers, and clinical endpoints.
Computational cost limits the number of patients, datasets, tasks, and hypotheses that researchers can study. To realize the full potential of pathology foundation models, we need models that can be applied repeatedly and affordably across large patient populations.
From GigaPath and GigaTIME to the -Flash family
GigaPath (Nature, 2024) (opens in new tab) is a whole-slide foundation model pretrained on large-scale real-world histopathology data from Providence. Unlike models that operate only at the tile level, GigaPath learns contextualized representations of entire slides, capturing both local cellular patterns and global tissue architecture.
GigaTIME (Cell, 2026) (opens in new tab) extends this line of work to tumor microenvironments. Trained on 40 million cells with paired H&E and multiplex immunofluorescence (mIF) data, GigaTIME translates routine H&E images into virtual spatial proteomics maps across 21 protein channels. Applied to over 14,000 cancer patients, it generated a virtual population that uncovered more than 1,200 statistically significant associations between immune cell states and clinical biomarkers.
GigaPath and GigaTIME addressed the scale of pathology data and biological discovery. And now, the Flash family of models addresses scale of experimentation.
Figure 1: Overview of the GigaPath/GigaTIME model family. GigaPath-Flash provides efficient tile and slide encoders at 22M parameters. GigaTIME-Flash predicts spatial proteomics from H&E, replacing the CNN backbone with the distilled ViT-S encoder. The Flash family shares a core design goal: preserving useful pathology representations while substantially reducing the computational resources required to generate and use them. Both models are built on a common efficient backbone — a compact ViT-S tile encoder distilled from the original billion-parameter GigaPath encoder — and both are released under the Apache 2.0 license.
GigaPath-Flash
GigaPath-Flash is an efficient foundation model for whole-slide representation learning. It combines a 22M-parameter ViT-S tile encoder with a 21M-parameter LongNet slide encoder. The tile encoder is distilled from the original GigaPath ViT-g teacher, transferring the representational capacity of a billion-parameter model into a backbone that is an order of magnitude smaller. The slide encoder contextualizes all tile embeddings via dilated attention, scaling linearly with the number of tiles.
On slide-level classification benchmarks (PANDA prostate grading and EBRAINS brain tumor subtyping), GigaPath-Flash achieves the lowest inference cost among whole-slide pretrained models while retaining competitive performance — scoring within 3% of the original GigaPath at roughly 50 times less compute.
Figure 2: Efficiency–performance trade-off on whole-slide benchmarks. GigaPath-Flash (red, top-left) achieves competitive performance at substantially lower computational cost than other whole-slide pretrained models. GigaTIME-Flash
GigaTIME-Flash replaces the CNN backbone of the original GigaTIME with the GigaPath-Flash ViT-S encoder, paired with a lightweight convolutional decoder for H&E-to-mIF translation. The model is fine-tuned using LoRA adapters, keeping the pretrained encoder weights largely frozen.
On both in-distribution and out-of-distribution cohorts spanning brain, breast, colon, and lung cancers, GigaTIME-Flash matches or improves upon the original GigaTIME in spatial protein prediction quality. The gains are particularly notable on out-of-distribution data, suggesting that the foundation model backbone improves generalization to previously unseen tissue types.
Figure 3: Mean windowed Pearson correlation for GigaTIME and GigaTIME-Flash on the GigaTIME test set and four out-of-distribution Prov-TMA cohorts. GigaTIME-Flash matches or improves upon the original across all cohorts. Efficiency without giving up the foundation
The efficiency gains of the Flash models are substantial:
Model Type Efficiency gain GigaPath-Flash Whole-slide Foundation Model ~50× less compute, 97% of predictive performance compared to GigaPath GigaTIME-Flash Spatial Proteomics ~6× faster, ~8× less memory, better predictive performance compared to GigaTIME For a single slide, these differences reduce runtime and hardware requirements. Across tens of thousands of slides, they can determine whether an experiment is practical at all. To illustrate, we estimate the wall-clock time for generating virtual mIF across cohorts of different sizes on a single A100 GPU, assuming approximately 10,000 tiles per slide:
Model 1,000 slides 100K slides 1M slides GigaTIME-Flash ~2 GPU-hours ~7 GPU-days ~70 GPU-days GigaTIME ~7 GPU-hours ~30 GPU-days ~300 GPU-days Estimates assume ~10,000 tiles per slide, batch size 128, single NVIDIA A100 GPU. Actual runtime depends on slide size, tiling resolution, and hardware. 
Figure 4: GigaTIME efficiency scaling. Left: throughput (tiles/sec) vs. batch size. Right: peak GPU memory (GB) vs. batch size. GigaTIME-Flash scales to over 1,600 tiles/sec while using a fraction of the memory. An open model release
Both GigaPath-Flash and GigaTIME-Flash are released as open-weight models under the Apache 2.0 license. Model weights and code are available on HuggingFace:
- GigaPath-Flash (opens in new tab)
- GigaTIME-Flash (opens in new tab)
- GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
This is an early research release. Our current evaluations cover a limited set of benchmarks and cohorts, and broader validation across tasks, scanners, and patient populations is still needed. We expect the most valuable applications of these models to include scientific questions, cohorts, and use cases beyond those in our initial experiments. We welcome community evaluation, and we are equally interested in reports of where these models work well and where they fall short.
Downstream clinical applications will require additional multi-institutional and prospective validation.
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Efficiency as an enabler of discovery
GigaPath and GigaTIME demonstrated what pathology foundation models can learn from whole slides and tumor tissue. GigaPath-Flash and GigaTIME-Flash are a step toward making those capabilities usable across larger populations, more experiments, and a broader research community.
By making pathology foundation models more efficient, we hope to expand the scale of the scientific questions researchers can ask.
Acknowledgements
GigaPath-Flash and GigaTIME-Flash are joint work across Microsoft Research, the University of Washington, and Providence. For technical details, see the paper.
Paper co-authors: Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao, Hanwen Xu, Jaspreet Bagga, Guanghui Qin, Robert E. Kramer, Cliff Wong, Soohee Lee, Hao Qiu, Theodore Zhengde Zhao, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Eduardo Alejandro Lozano Garcia, Naiteek Sangani, Alberto Santamaria-Pang, Maximilian Rokuss, Yashna Hasija, Naisargi Manishkumar Patel, Jason Entenmann, Alexandra Q. Bartlett, Bill J. Wright, Bernard A. Fox, Brian Piening, Sheng Zhang, Sheng Wang, Tristan Naumann, Carlo Bifulco, Hoifung Poon
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Gemini Omni 1.1 Flash lets you build with more control
Omni now delivers studio-quality video production, including the ability to extend a scene, first and last frame interpolation, crisp 4K upscaling, faster prototyping, and more.
Anish Nangia
Product Manager, Google DeepMind
Alisa Fortin
Product Manager, Google DeepMind
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This content is generated by Google AI. Generative AI is experimental
Today, we’re introducing Gemini Omni 1.1 Flash, a new suite of creative controls and generative video capabilities to support developers. Gemini Omni brought real-world reasoning to generative creation, and today’s updates make Omni 1.1 production-ready for professional use via the Gemini API in Google AI Studio.
Whether you’re building generative video workflows, creative tools, or media editing software, these updates make generative video more controllable, faster to iterate on, and polished for real-world deployment. Here’s a look at what’s new:
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.
Prompt 1: A close-up low-angle shot of a stylish drummer in a beige suit playing a red drum kit in a grand hall transitions as the camera whip-pans to the side, revealing an older saxophonist playing alongside a ballet dancer spinning in a white outfit under soft purple stage lights. One continuous shot, no jump cuts.
Prompt 2: The camera zooms into the TV screen, where we see the same woman and the same scene from the beginning. Seamless video. One continuous shot, no jump cuts.
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
Prompt: A microscopic view of iridescent marine diatoms, displaying intricate, glass-like silica shells with breathtaking natural symmetry. The colors range from deep volcanic amber and warm copper to vibrant turquoise and violet, mimicking the rich palette of earth and ocean. Tiny, delicate structures glow softly against a clean dark field background. High-fidelity scientific imaging, sharp details, organic textures, micro-photography. Maintain the microscope lens effect throughout the entire video.
Upscale up to 4K resolution
Generate polished, high-resolution 1080p or 4K outputs that are ready for professional production with Omni 1.1.
Prompt 1: Fish swimming, tracking shot
Prompt 2: A little chipmunk darting out of the woods from the left side of the screen and sniffing the air inquisitively before darting out of frame on the right side
Prompt 3: Cinematic macro close-up of vibrant golden-orange Japanese maple leaves on a delicate branch, gently rustling and swaying in a soft, rhythmic autumn breeze. Sunlight filters through the translucent foliage, creating a warm, glowing effect. Shallow depth of field, dreamy bokeh background, hyper-detailed textures, photorealistic, 4k.
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.
Prompt: Use the three uploaded videos of dancers and replace them with the provided characters. Have them perform their individual dances from the reference videos, all together in the large, open space from the provided image.
The dog character dog.png should do the classical dance from dance3.mp4. The octopus octo.png should do the hip hop dance from dance1.mp4, and the bear bear.png should do the breakdance from dance2.mp4. The final result should be one continuous shot with no scene cuts.
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
Pricing table for Gemini Omni 1.1 Flash.
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.
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