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Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
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Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
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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.
Senior Director, Product Management
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 built this game with a simple prompt using a looping instruction in Google Antigravity. The game uses puzzles, environmental storytelling, and textures generated with Nano Banana to create an immersive 3D level in which you play a wizard navigating a castle.
Gemini 3.8 Flash builds a fully functional DOS version of Google Maps in a single prompt in Google Antigravity that is fully playable with locations, directions, and Street View.
Explore realtime cross-sections, 2D projections, scientific explanations in a topographic map of famous geographical sites built with Gemini 3.8 Flash in Google Antigravity using real datasets from the U.S. Geological Survey.
Hardware Anatomy is an interactive 3D visualizer built with Gemini 3.8 Flash in Google AI Studio that generates realistic Three.js renderings of physically-proportioned teardowns for hardware devices. It automatically decomposes devices into layers you can explode and inspect with a deconstruction slider.
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%. 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. Links
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