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Gemini Enterprise: Gemini 4 Argon available in web app, mobile app, and AI developer tools

GAyesterdayUnverifiedAdded Oct 14, 2026

Gemini 4 Argon is generally available (GA) for the Gemini Enterprise web app, mobile app, and AI developer tools. Location availability : Supported in global , us , and eu multi-regions, with data residency and ML processing support in us and eu . For more information, see Data residency for Gemini Enterprise . App availability : Turned off by default.

Topics: LLMs, Regions

Google Cloud's release notes

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Gemini Enterprise: Gemini 4 Argon is available in AlphaEvolve

AlphaEvolve experiments can generate candidate programs with Gemini 4 Argon in the global , us , and eu regions. To use it, specify gemini-4-argon in the models field of generationSettings . Gemini 3.8 Flash remains the default model. For more information, see Supported models and AlphaEvolve limitations .

GAunverified

Gemini Enterprise: Control access to Antigravity features

Administrators can control whether users in their organization have access to the following Google Antigravity features: Boost : Uses a tiered multi-agent hierarchy to solve complex algorithmic challenges and deep debugging tasks with the /boost command. This feature is generally available (GA) in Antigravity.

Updateunverified

Gemini Enterprise: Gemini 2.5 Pro removal in Canada and Japan on October 20, 2026

On October 20, 2026, Gemini 2.5 Pro will be removed from the Gemini Enterprise app in the Canada and Japan in-country regions. After this date, users in these regions can't select Gemini 2.5 Pro in the Gemini Enterprise app. Gemini 2.5 Pro remains available in the global , us , and eu regions. For more information, see Data residency for Gemini Enterprise .

Also shipped on Oct 13, 2026

Google Cloud in October 2026
Betaunverified

What's included:

- Three training methods: Supervised fine-tuning (SFT) , direct preference optimization (DPO) , and reinforcement learning with group relative policy optimization (GRPO) . Each method trains a low-rank adapter (LoRA) or the full model weights. Custom loss functions : Train with any objective you compute in PyTorch from the model's log-probabilities.