Alphabet is releasing three new Gemini models on Tuesday, including its clearest answer yet to Anthropic‘s lead in cybersecurity, as the company looks to show progress across a product pipeline that has faced delays and mounting competition.
Gemini 3.5 Flash Cyber is designed to detect and patch software vulnerabilities and will initially be available only to governments and trusted partners through a limited-access pilot. Google said the specialized model runs at a lower price per token than larger models.
That could help Google narrow its cybersecurity gap with Anthropic, which has built an early lead in automated code defense.
Google is also launching Gemini 3.6 Flash, which improves coding, multimodal and knowledge-work performance while using up to 17% fewer tokens and costing less per token than the previous model — a meaningful reduction in the cost of running high-volume workloads.
Gemini 3.5 Flash-Lite, meanwhile, is Google’s fastest and least expensive model in the 3.5 family, built for high-volume workloads and smaller tasks within larger AI-agent systems.
The broader lineup reflects Google’s bet that price and efficiency can help offset its slower timing in several key product categories.
Artificial Analysis data shows Gemini Flash already undercuts comparable models from Anthropic, OpenAI and Chinese rivals on cost. According to the company, Gemini 3.6 Flash — the stronger of the two new models — is cheaper per task than GPT-5.6 Terra Max, Kimi K3 and Qwen 3.7 Max, while 3.5 Flash-Lite costs just a fraction of that.
The rollout comes on the eve of Alphabet earnings and as Chinese rivals gain momentum. Moonshot AI’s Kimi K3 drew enough demand that the company limited new subscriptions and API access because of capacity constraints, while Alibaba is teasing Qwen 3.8 Max, which it said trails only Anthropic’s Fable 5 in overall performance.
That demand highlights the other side of the AI race: Building a competitive model is only part of the challenge. Companies also need enough computing capacity to serve it at scale.
Google has a potential advantage through its custom chips, cloud infrastructure and ability to design models and hardware together, although the company has faced capacity constraints of its own.
Tuesday’s model launches come as Google is reportedly developing a specialized chip designed to run Gemini up to 10 times more efficiently, part of a broader push to lower the cost of serving AI.
A Google Cloud spokesperson told CNBC in a statement that its teams are “constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers” and that “while not every project moves into production, this rigorous exploration is central to our full stack approach.”
“By co-designing our hardware and software from the ground up, we ensure our systems are integrated and highly optimized for real-world workloads,” continued the statement.
Google is also offering more visibility into its roadmap after questions about delays. Gemini 3.5 Pro is now being tested with partners ahead of broader availability, while the company has begun its largest-ever pre-training run for Gemini 4.
