Google DeepMind launches Gemma 4 open models with Apache 2.0 licensing and native agent features

In an April 2 post on X, Google DeepMind framed Gemma 4 as a new open model family designed to run on hardware developers already control. The accompanying official blog post makes the positioning explicit: Gemma 4 is built for advanced reasoning and agentic workflows, and Google is releasing it under an Apache 2.0 license. In a market where open models often force a trade-off between raw performance and deployment freedom, Google is clearly trying to claim both.

The lineup is broad enough to matter across devices. Gemma 4 ships as Effective 2B, Effective 4B, 26B Mixture of Experts, and 31B Dense. Google says the 31B model ranks as the #3 open model on Arena AI’s text leaderboard and the 26B model as #6, while outperforming some models 20 times its size. At the same time, the smaller E2B and E4B variants are positioned for edge deployment on phones, Raspberry Pi, and NVIDIA Jetson-class hardware, making the family relevant beyond workstation inference.

Google is packaging Gemma 4 as an agent-ready open stack

The more important signal is not just the benchmark score. Google says Gemma 4 includes native function calling, structured JSON output, and native system instructions so developers can build autonomous workflows that interact with tools and APIs more reliably. The company also highlights offline code generation, multimodal input, 128K to 256K context windows, and training across more than 140 languages. That combination pushes Gemma 4 well past “open chat model” territory and into local-first agent infrastructure.

Why this matters is straightforward. Enterprises get a commercially permissive license and more control over where the model runs, while developers get day-one support across Hugging Face, Ollama, NVIDIA NIM, NeMo, and other tooling. Open models are no longer competing only on leaderboard screenshots; they are competing to become the base layer for deployable agent systems. On that metric, Gemma 4 is one of the clearer statements from a major lab this year. Sources: the X thread and Google’s launch post.