Google Antigravity Integrations

Gemini 3.1 Flash-Lite is Google’s latest high-performance AI model optimized for large-scale, cost-sensitive workloads. As the fastest and most economical model in the Gemini 3 lineup, it is built to support developers who require rapid responses and predictable pricing. The model’s pricing structure—$0.25 per million input tokens and $1.50 per million output tokens—positions it as an efficient solution for production-grade deployments. It demonstrates a 2.5x faster time to first answer token compared to Gemini 2.5 Flash, along with a 45% improvement in output speed. These latency gains make it especially suitable for real-time applications and interactive systems. Performance benchmarks reinforce its competitiveness, including an Arena.ai Elo score of 1432 and strong results across reasoning and multimodal understanding tests. In several evaluations, it surpasses comparable models and even exceeds earlier Gemini generations in quality metrics. Developers can dynamically adjust the model’s “thinking levels,” offering control over reasoning depth to balance speed and complexity. This adaptability supports a wide spectrum of tasks, from high-volume translation and content moderation to generating complex user interfaces and simulations. Early adopters have reported that the model handles intricate instructions with precision while maintaining efficiency at scale. The model is accessible through the Gemini API in Google AI Studio and via Vertex AI for enterprise deployments. By combining affordability, speed, and adaptable intelligence, Gemini 3.1 Flash-Lite delivers scalable AI performance tailored for modern development environments.

Gemini for Science revolutionizes scientific discovery by providing AI-powered tools and resources tailored to enhance scientific projects. By combining experimental tools from Google Labs with the scientific workflows available in Google Antigravity, it seeks to accelerate research efforts, enhance analytical capabilities, and empower researchers to explore the future of AI-driven scientific inquiry. The Literature Insights feature aggregates scholarly articles to identify new research opportunities, generate robust research outputs, and transform paper data into organized tables connected to original sources. Simultaneously, Hypothesis Generation utilizes a multi-agent strategy that mimics the scientific method, enabling it to identify gaps in knowledge, propose promising research paths, and outline testable research methodologies that have the potential to yield significant advancements. Furthermore, Computational Discovery aids researchers in pinpointing models and algorithms through a smart research engine that develops and assesses code variations based on user-defined optimization goals, thus further streamlining the research workflow. Overall, these cutting-edge tools are designed not just to enhance the efficiency of scientific research but also to fundamentally change the way it is perceived and executed. The integration of these advanced features signifies a major leap forward in the collaboration between AI and scientific exploration.

The Nano Banana 2 Lite is Google's quickest Gemini Image model in the Nano Banana lineup, designed for outstanding speed, scalability, and throughput. Known as the Gemini 3.1 Flash Lite Image, it is specifically tailored for rapid ideation and fast-paced developer workflows that emphasize quickness, swift iterations, and streamlined production methods. This model is recommended as an upgrade over its predecessor, the original Nano Banana, enabling developers to gain immediate benefits in crucial performance areas while improving their image generation and editing processes via Google AI Studio, Gemini API, and the Gemini Enterprise Agent Platform. Optimized for near-real-time, high-volume applications where ultra-low latency is critical, the Nano Banana 2 Lite can produce text-to-image outputs in just seconds, making it perfect for interactive prototyping, visual drafting, creative experimentation, and large-scale image generation. As the need for speed and efficiency in image processing continues to escalate, this model emerges as a vital resource for developers who aim to elevate their creative capacities and push the boundaries of their projects even further. Its innovative features position it as a pivotal element in modern development environments.

Gemini 3.8 Flash Cyber is the latest and most sophisticated cybersecurity framework developed by Google, delivering unparalleled efficiency in detecting vulnerabilities and automating patch management with impressive speed for quick iterations. Designed specifically for reliable defenders, it is made available through the Fairwind Program. On CyberGym, a well-respected benchmark in the industry for vulnerability detection, this model demonstrates outstanding capabilities in autonomous vulnerability identification, surpassing both its predecessor, Gemini 3.5 Flash Cyber, and larger frontier models. Additionally, Google evaluated its performance on an internal benchmark that encompasses intricate codebases across 20 different programming languages, attaining a remarkable success rate exceeding 70% in identifying a range of vulnerabilities. Unlike many other models that prioritize offensive tactics, Gemini 3.8 Flash Cyber centers on the critical task of remediation, equipping defenders with sophisticated tools that bolster their defenses against cyber threats. This emphasis on proactive measures signifies an important evolution in the field of cybersecurity, shifting the focus from merely exploiting weaknesses to actively protecting systems and data. As cyber threats continue to evolve, the need for such a defensive strategy becomes increasingly vital for organizations seeking to enhance their security posture.

Harden AIF is a dedicated security solution tailored for AI coding agents, focusing on safeguarding agent endpoint security by evaluating tool calls before they are executed, taking into account the developer's intent, the context of the session, the organization’s policies, and the potential consequences of the proposed action. This platform is adept at protecting against various threats, including harmful commands, unauthorized access, accidental data transfers, exposure of sensitive information, misuse of privileges, and other potentially dangerous actions performed by agents. By allowing legitimate operations to continue smoothly, it also ensures the safe redaction of sensitive data during approved workflows while blocking actions that stray from the developer's original intent or surpass their authority. Harden AIF's compatibility with a range of popular coding agents and development tools, such as Claude Code, Codex, Cursor, Antigravity CLI, Kiro, Hermes, and OpenClaw, establishes a consistent security framework across the agent ecosystem. This thorough methodology not only bolsters security measures but also cultivates a more secure environment for developers engaging with AI technologies. Furthermore, its ability to adapt and integrate with emerging tools signifies a forward-thinking approach in the ever-evolving landscape of AI development.