Google DeepMind 发布世界模型 Genie 4

October 27, 2026 Models

Raia Hadsell, Diego Rivas and Hang Qi

Today, we are announcing Genie 4, our new world model, which unlocks unprecedented realism and improves real-time interactive simulation. Genie 4 is natively multimodal, enabling audio-like music, ambient noises, and interactive dialogue to be simultaneously generated with the world.

The Genie 4 model incorporates and builds on capabilities from the Gemini family: Gemini Omni anchors the simulations in real-world appearance and behavior, while other Gemini models provide the complex reasoning needed to help maintain a simulation’s coherence and continuity.

With this foundation, you can set an event in motion, and Genie 4 simulates what happens next: how the world responds, how it sounds, and how the people in it move, react, and speak.

Understanding and simulating the world is a crucial aspect of building AGI. Genie 4 advances our ability to create safe, infinitely variable simulations that can help with everything from developing training environments for robots and agents to unlocking the creation of new interactive experiences.

We have started rolling Genie 4 out as a research preview for trusted testers — gathering feedback before we release it to consumers and developers, starting with Google AI Ultra subscribers in Project Genie and early access partners of the Gemini API and Google AI Studio. Join the waitlist for Genie 4 in Project Genie here.

Genie 4 advances four key capabilities

The following are recordings of real-time interactions from Genie 4, where users use standard keyboard controls for navigation and camera control, and text prompts to steer the scene as it evolves.

Shaping world simulation in real-time

With Genie 4, you can use natural language to modify a simulation in real-time — like changing the weather, moving the camera, or introducing a new person or object — and Gemini will contextually adapt your prompt to match the surrounding scene. Between your inputs, Gemini also monitors and steers the scene to keep the world progressing cohesively.

Turn on audio to hear Genie 4 worlds.

Pushing the frontier in realism for physics and human action

Genie 4 introduces a new standard of realism to real-time interactive simulation. Built on an advanced physical understanding of the world, Genie 4 simulates real-world physics scenarios, such as aerodynamics, collisions, and more.

The most dramatic leap is how Genie 4 simulates people, creating characters that move naturally, respond organically to their environments, and can be interactively steered in real-time.

Modeling how the world sounds

Genie 4 is our first real-time world model to generate audio natively. Every world carries the ambient sound of its own setting, from wind in the trees to waves crashing at the beach.

The true frontier, however, is human speech. Genie 4 introduces interactive dialogue that adapts on the fly. Characters converse and react to your inputs or events in the world, all in real-time.

Simulating complex events with Gemini models

Bringing Gemini and Genie 4 together adds a new layer of reasoning to world simulation, allowing us to model complex, multi-step events and test how AI systems navigate different environments before bringing them to the real world.

To achieve this in real-time, Gemini 3.8 Flash serves as a guiding agent in the loop: as Genie 4 generates a world in real-time, Gemini continuously observes the scene, breaks down goals into a sequence of steps, and reviews incoming frames to track the progress and verify that each step succeeded. As the world shifts or the user intervenes, Gemini adapts its plan in the moment, unlocking worlds with reliable causality, procedural behaviors, and multi-step reasoning.

As part of exploring this capability, we have also been experimenting with Gemini 4 Argon and Genie 4 to bring our frontier research together with Gemini Robotics.

In the example below, Argon breaks down a user-instructed task into steps for Genie to render. These steps are interleaved as text captions to Genie 4 as the simulation is rendering. Because Genie 4 can be steered in real-time with text captions using Gemini, this now unlocks the path of adapting the simulation as it progresses towards the completion of that task. This capability becomes a path for simulating a breadth of scenarios in imagined worlds, using Gemini 4 Argon and Genie 4.

Learnings from trusted testers

While Genie 4 is a major research milestone, it remains an early, experimental model. To understand how interactive world models can support real-world workflows—and where they still need to grow—we’ve been working with cohorts of trusted testers across creative industries, journalism, education, gaming, and engineering.

Testers highlighted how the leap in photorealism, native audio, and real-time steering allows ideas and concepts to be turned into realistic simulations, unlocking new abilities across disciplines:

• Creative direction: Creatives and technologists are testing Genie 4 as a rapid "draft mode" for pre-visualization and ideation. For example, filmmakers can step into a live simulation of a scene and run multiple "takes" in real-time, adjusting camera angles, lighting, dialogue, and action on the fly.
• Historical scenarios & immersive photographs: Photojournalists are exploring how a single 2D archival photograph can become a grounded simulation of that environment in seconds. Teams are testing how populated, dynamic worlds can help experience historical spaces or visualize how short-term environmental events, like a flash flood or wildfire, unfold.
• Interactive experience concept design: Game developers and creative technologists are testing Genie 4 to go from an initial idea to an explorable prototype in seconds, testing visual styles, material aesthetics, and interactive mechanics through natural language prompts before writing a single line of engine code.
• Simulation: Aside from our robotics research, enterprise partners across industries are evaluating Genie 4 as a tool for rapid "zero-to-one" scenario generation, and identifying gaps to further improve Genie to best fulfill that simulation need.

Future Work: Towards hybrid world models

A promising path for future progress lies in hybrid world models, which combine the logical, structured reasoning of code and language models with the rich, sensory representation of pixel-level simulation.

Bringing Genie and Gemini models closer together is an early step in this direction. Gemini works behind the scenes to map the structure and plan the narrative timeline, while Genie generates the immediate physical reality—rendering how the world looks, sounds, and responds, pixel by pixel. Together, these complementary strengths can unlock a new class of world models that are far more coherent, controllable, and grounded than either approach could achieve alone.

Building world models responsibly

Genie 4 marks a new area of frontier research that merges real-time generation and photorealism, introducing a new range of capabilities and dimensions that we want to roll out and explore in a responsible way. Genie 4 includes strict safety guardrails that are consistent with our safety policies across other models. Before launching this preview, we evaluated Genie 4 through internal rigorous safety evaluations to prevent the generation of harmful or malicious content. Also, we have intentionally deployed it first to trusted tester partners to get early feedback, before we release the model more broadly.

To maintain transparency, all video and audio generated by Genie 4 will include SynthID, our imperceptible digital watermarking technology that helps identify AI-generated content across visual and audio modalities.

Understanding current limitations

Genie 4 remains an early, evolving area of research.

While it can maintain coherent environments over short sessions, visual details and object permanence can still shift or drift over time—especially as a simulation runs longer, after multiple complex prompt edits, or when navigating throughout the scene. Complex physical interactions, real-time audio accuracy, rendering legible text, spatial accuracy, and long-term consistency are active areas of ongoing research. We are sharing Genie 4 as a preview to learn alongside our community and safely advance the frontier of interactive simulation.

Next steps for Genie 4

Genie 4 has been released as a research preview to trusted testers, and is coming soon to Google AI Ultra subscribers in Project Genie and early access partners in the Gemini API and Google AI Studio. Join the waitlist for Genie 4 in Project Genie here.

We’re excited for users to step inside, experiment, and build their own interactive worlds soon.

Acknowledgements

Genie 4 was made possible by core contributions from Hang Qi, Diego Rivas, Aleksander Hołyński, Inbar Mosseri, Jack Parker-Holder, Shlomi Fruchter, Tejas Kulkarni, James Assiene, Philip Ball, Jakob Bauer, Eddie Cao, Lucy Chai, Harris Chan, Kleopatra Chatziprimou, Hao Chen, Yoni Choukroun, Mark Collier, Enric Corona, Kurtis David, Ariel Ephrat, Maxime Gazeau, Ruiqi Gao, Chelsee Glover-Odom, Liangke Gui, Swaroop Guntupalli, Shan Han, Tingbo Hou, Jiri Hron, Hui Huang, Drew A. Hudson, Zhouyuan Huo, Linyi Jin, Christos Kaplanis, Dhrupad Karwa, Eunyoung Kim, Congtao Kuang, Eliot Laidlaw, José Lezama, Hao Li, Shuang Li, Yuchen Liu, Hanzi Mao, Mark Matthews, Matt McGill, Kay McKinney, Ricardo Monti, Mark Murphy, Joseph Ortiz, Roni Paiss, Frank Perbet, Ben Poole, Tal Reiss, Konstantinos Rematas, Guy Scully, Jeremy Shar, Shashank Shastry, Sri S, Omer Tov, Ruben Villegas, Andrey Voynov, Hongjie Wang, Luyu Wang, Kaustubh Sridhar, Mo Samsami, Stephen Spencer, Yufei Wang, Will Whitney, Rundi Wu, Joyce (Jingjing) Xie, Jesse Yin, Shiran Zada, Serena Zhang, Xiaofan Zhang, Zhengqi Li, Zhixing Zhang, Yuan Zhong, Shuo-yiin Chang, Dmitry Lagun, Ronny Huang, Yao Su, Ankush Gupta, Genzhi Ye, Haolin Jia, Weiqin Wang, Xi Xiong, Salah Zaiem, Carol Zheng, .

Genie 4 also required close collaborations across partner teams, we thank all our cross-functional partners that made this launch possible: Dina Bashkirova, Emil Bergner, Greta Borisko, Irina Blok, Melissa Byun, Gabe Clapper, Viral Carpenter, Michael Y. Chang, Maximo Cudich-Sieburger, Chloe Doan, Alisa Fortin, Shivani Ghanta, Vikram Gopali, Ezra Gorman, Khyatti Gupta, Sam Hasinoff, Philipp Henzler, Hector Hinestroza, Ed Hirst, Tyler Holland, Kristian Holsheimer, Tiffany Hu, Kapil Joshi, Randeep Katari, Pavan Kumar, Vikhyat Kumeria, Sam Kvaalen, Matthew Levine, Yinxiao Li, Yuchi Liu, Miao Luo, Nick Matarese, Kangfu Mei, Signe Nørly, Moises Oliveira, Eric Paskie, Manika Puri, Susanna Ricco, Andrey Ryabtsev, Cai Schumann, Nicole Brichtova, Andy Sekyere, Grace Shih, Melissa Smith, Duncan Noble Smith, Kenda Stewart, Rod Strougo, Jenn Sturgeon, Amy Zeng, Gaby Pearl, Eleanor Tomlinson, Kenyon Watson, Inês Ayer, Luke Talbot, Han-Yun Tsao, Guillaume Vernade, Karen Wang, Changchang Wu, Ian Wilkinson, Weijuan Xi, Jennifer Yick, Yunhan Zhao, Eric Zhang, Jason Zhang, Annie Zhou, James Zhou, Chris Walker, Alan Yam, Alex Chen, Cliff Lungaretti, Dan Motzenbecker, Kaloyan Kolev, Matthew Carey, Nate Zucker, Sam Lawton, Shashwath Santosh, Suz Chambers.

Thanks to Raia Hadsell, Aäron van den Oord, Josh Woodward, Steve Seitz, Carlos Hernandez, Elias Roman, Jung E. Kim, Abhishek Bapna, Eli Collins, Tulsee Doshi, Carolina Parada, Jie Tan, Koray Kavukcuoglu and Demis Hassabis for their insightful guidance and support throughout the research process.

Lastly, we thank our trusted tester and early access users that have provided feedback to help guide our research forward.