We are also sharing the frontiers of our agentic research by showcasing prototypes enabled by Gemini 2.0’s native multimodal capabilities.
Gemini 2.0 Flash builds on the success of 1.5 Flash, our most popular model yet for developers, with enhanced performance at similarly fast response times. Notably, 2.0 Flash even outperforms 1.5 Pro on key benchmarks, at twice the speed. 2.0 Flash also comes with new capabilities. In addition to supporting multimodal inputs like images, video and audio, 2.0 Flash now supports multimodal output like natively generated images mixed with text and steerable text-to-speech (TTS) multilingual audio. It can also natively call tools like Google Search, code execution as well as third-party user-defined functions.
Our goal is to get our models into people’s hands safely and quickly. Over the past month, we’ve been sharing early, experimental versions of Gemini 2.0, getting great feedback from developers.
Gemini 2.0 Flash is available now as an experimental model to developers via the Gemini API in Google AI Studio and Vertex AI with multimodal input and text output available to all developers, and text-to-speech and native image generation available to early-access partners. General availability will follow in January, along with more model sizes.
To help developers build dynamic and interactive applications, we’re also releasing a new Multimodal Live API that has real-time audio, video-streaming input and the ability to use multiple, combined tools. More information about 2.0 Flash and the Multimodal Live API can be found in our developer blog.
Also starting today, Gemini users globally can access a chat optimized version of 2.0 Flash Experimental by selecting it in the model drop-down on desktop and mobile web and it will be available in the Gemini mobile app soon. With this new model, users can experience an even more helpful Gemini assistant.
Early next year, we’ll expand Gemini 2.0 to more Google products.
Gemini 2.0 Flash’s native user interface action-capabilities, along with other improvements like multimodal reasoning, long context understanding, complex instruction following and planning, compositional function-calling, native tool use and improved latency, all work in concert to enable a new class of agentic experiences.
The practical application of AI agents is a research area full of exciting possibilities. We’re exploring this new frontier with a series of prototypes that can help people accomplish tasks and get things done. These include an update to Project Astra, our research prototype exploring future capabilities of a universal AI assistant; the new Project Mariner, which explores the future of human-agent interaction, starting with your browser; and Jules, an AI-powered code agent that can help developers.
We’re still in the early stages of development, but we’re excited to see how trusted testers use these new capabilities and what lessons we can learn, so we can make them more widely available in products in the future.
Since we introduced Project Astra at I/O, we’ve been learning from trusted testers using it on Android phones. Their valuable feedback has helped us better understand how a universal AI assistant could work in practice, including implications for safety and ethics. Improvements in the latest version built with Gemini 2.0 include:
Better dialogue: Project Astra now has the ability to converse in multiple languages and in mixed languages, with a better understanding of accents and uncommon words.
New tool use: With Gemini 2.0, Project Astra can use Google Search, Lens and Maps, making it more useful as an assistant in your everyday life.
Better memory: We’ve improved Project Astra’s ability to remember things while keeping you in control. It now has up to 10 minutes of in-session memory and can remember more conversations you had with it in the past, so it is better personalized to you.
Improved latency: With new streaming capabilities and native audio understanding, the agent can understand language at about the latency of human conversation.
We’re working to bring these types of capabilities to Google products like Gemini app, our AI assistant, and to other form factors like glasses. And we’re starting to expand our trusted tester program to more people, including a small group that will soon begin testing Project Astra on prototype glasses.
Project Mariner is an early research prototype built with Gemini 2.0 that explores the future of human-agent interaction, starting with your browser. As a research prototype, it’s able to understand and reason across information in your browser screen, including pixels and web elements like text, code, images and forms, and then uses that information via an experimental Chrome extension to complete tasks for you.
When evaluated against the WebVoyager benchmark, which tests agent performance on end-to-end real world web tasks, Project Mariner achieved a state-of-the-art result of 83.5% working as a single agent setup.
It’s still early, but Project Mariner shows that it’s becoming technically possible to navigate within a browser, even though it’s not always accurate and slow to complete tasks today, which will improve rapidly over time.
To build this safely and responsibly, we’re conducting active research on new types of risks and mitigations, while keeping humans in the loop. For example, Project Mariner can only type, scroll or click in the active tab on your browser and it asks users for final confirmation before taking certain sensitive actions, like purchasing something.
Trusted testers are starting to test Project Mariner using an experimental Chrome extension now, and we’re beginning conversations with the web ecosystem in parallel.
Next, we’re exploring how AI agents can assist developers with Jules — an experimental AI-powered code agent that integrates directly into a GitHub workflow. It can tackle an issue, develop a plan and execute it, all under a developer’s direction and supervision. This effort is part of our long-term goal of building AI agents that are helpful in all domains, including coding.
More information about this ongoing experiment can be found in our developer blog post.
Google DeepMind has a long history of using games to help AI models become better at following rules, planning and logic. Just last week, for example, we introduced Genie 2, our AI model that can create an endless variety of playable 3D worlds — all from a single image. Building on this tradition, we’ve built agents using Gemini 2.0 that can help you navigate the virtual world of video games. It can reason about the game based solely on the action on the screen, and offer up suggestions for what to do next in real time conversation.
We're collaborating with leading game developers like Supercell to explore how these agents work, testing their ability to interpret rules and challenges across a diverse range of games, from strategy titles like “Clash of Clans” to farming simulators like “Hay Day.”
Beyond acting as virtual gaming companions, these agents can even tap into Google Search to connect you with the wealth of gaming knowledge on the web.
In addition to exploring agentic capabilities in the virtual world, we’re experimenting with agents that can help in the physical world by applying Gemini 2.0's spatial reasoning capabilities to robotics. While it’s still early, we’re excited about the potential of agents that can assist in the physical environment.
You can learn more about these research prototypes and experiments at labs.google.
Gemini 2.0 Flash and our research prototypes allow us to test and iterate on new capabilities at the forefront of AI research that will eventually make Google products more helpful.
As we develop these new technologies, we recognize the responsibility it entails, and the many questions AI agents open up for safety and security. That is why we are taking an exploratory and gradual approach to development, conducting research on multiple prototypes, iteratively implementing safety training, working with trusted testers and external experts and performing extensive risk assessments and safety and assurance evaluations.
For example:
As part of our safety process, we’ve worked with our Responsibility and Safety Committee (RSC), our longstanding internal review group, to identify and understand potential risks.
Gemini 2.0's reasoning capabilities have enabled major advancements in our AI-assisted red teaming approach, including the ability to go beyond simply detecting risks to now automatically generating evaluations and training data to mitigate them. This means we can more efficiently optimize the model for safety at scale.
As Gemini 2.0’s multimodality increases the complexity of potential outputs, we’ll continue to evaluate and train the model across image and audio input and output to help improve safety.
With Project Astra, we’re exploring potential mitigations against users unintentionally sharing sensitive information with the agent, and we’ve already built in privacy controls that make it easy for users to delete sessions. We’re also continuing to research ways to ensure AI agents act as reliable sources of information and don’t take unintended actions on your behalf.
With Project Mariner, we’re working to ensure the model learns to prioritize user instructions over 3rd party attempts at prompt injection, so it can identify potentially malicious instructions from external sources and prevent misuse. This prevents users from being exposed to fraud and phishing attempts through things like malicious instructions hidden in emails, documents or websites.
We firmly believe that the only way to build AI is to be responsible from the start and we'll continue to prioritize making safety and responsibility a key element of our model development process as we advance our models and agents.
Gemini 2.0, AI agents and beyond
Today’s releases mark a new chapter for our Gemini model. With the release of Gemini 2.0 Flash, and the series of research prototypes exploring agentic possibilities, we have reached an exciting milestone in the Gemini era. And we’re looking forward to continuing to safely explore all the new possibilities within reach as we build towards AGI.
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