Gemini 3.8 Flash and Gemini 3.8 Flash Cyber is a Large Language Models (LLMs) tool. Fast AI model for coding, agents, and reasoning with cybersecurity variant. Key features include Advanced Software Engineering, Agentic Task Performance, and Multimodal Input Support. Best for software developers and engineers, data scientists and analysts and scientists and researchers.
About Gemini 3.8 Flash and Gemini 3.8 Flash Cyber
Gemini 3.8 Flash and Gemini 3.8 Flash Cyber are Google DeepMind's latest AI models released in September 2026. The standard Flash model excels at software engineering, agentic tasks, and multi-step reasoning. The Cyber variant focuses on vulnerability detection and automated patching.
Key Features
Advanced Software Engineering.
Agentic Task Performance.
Multimodal Input Support.
Cybersecurity Variant.
Competitive Pricing.
Tunable Reasoning Levels.
Frequently Asked Questions
Gemini 3.8 Flash is Google DeepMind's most intelligent Flash model, released in September 2026. It delivers significant improvements in software engineering, agentic tasks, and multi-step reasoning compared to its predecessor, while maintaining the same speed and low cost. The model supports multimodal input and a 1 million token context window.
Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens through December 31, 2026. Starting January 1, 2027, pricing doubles to $1.50 per million input tokens and $7.50 per million output tokens. Cached input tokens are available at $0.075 per million tokens for enterprise users.
Gemini 3.8 Flash Cyber is a specialized variant focused on cybersecurity applications, specifically vulnerability detection and automated patching. It demonstrates frontier-level performance on the CyberGym benchmark and can navigate complex codebases across 20 programming languages. Access is restricted to trusted defenders through Google's Fairwind Program, including government authorities and critical infrastructure operators.
Gemini 3.8 Flash is best suited for developers running autonomous agents, long coding sessions, and complex enterprise workflows. It excels in quantitative and professional fields like finance and legal analysis. Teams that prioritize result quality over token efficiency will benefit most, especially for long-horizon software engineering and multi-step reasoning tasks.
