Google has not only released Gemma 4, it has done so under the Apache 2.0 license. This is a significant change as it, as Google points out, opens up “Gemma 4’s applicability and usefulness, providing well-understood terms for modification, reuse, and further development”.
But the change in licensing is just the start of things for the open-weight model – there are in fact four distinct models catering for different needs, different circumstances, and different scales.
Google is pitching this release as a development of the Gemmaverse, saying that it is “purpose-built for advanced reasoning and agentic workflows”. There is a strong mobile focus, and Google is looking to land on Android devices and laptop GPUs.
Talking about the various models, Google says in an announcement:
We are releasing Gemma 4 in four versatile sizes: Effective 2B (E2B), Effective 4B (E4B), 26B Mixture of Experts (MoE) and 31B Dense. The entire family moves beyond simple chat to handle complex logic and agentic workflows. Our larger models deliver state-of-the-art performance for their sizes, with the 31B model currently ranking as the #3 open model in the world on the industry-standard Arena AI text leaderboard, and the 26B model securing the #6 spot. There, Gemma 4 outcompetes models 20x its size. For developers, this new level of intelligence-per-parameter means achieving frontier-level capabilities with significantly less hardware overhead.
At the edge, our E2B and E4B models redefine on-device utility, prioritizing multimodal capabilities, low-latency processing and seamless ecosystem integration over raw parameter count.
In a video, Olivier Lacombe – Group Product Manager at Google DeepMind – goes into some detail about what is new in Gemma 4:
There has been work put into making Gemma 4 suitable for as wide a range of hardware as possible, but Google is also keen to point out what makes this release so special:
- Advanced reasoning: Capable of multi-step planning and deep logic, Gemma 4 demonstrates significant improvements in math and instruction-following benchmarks that require it.
- Agentic workflows: Native support for function-calling, structured JSON output, and native system instructions enables you to build autonomous agents that can interact with different tools and APIs and execute workflows reliably.
- Code generation: Gemma 4 supports high-quality offline code, turning your workstation into a local-first AI code assistant.
- Vision and audio: All models natively process video and images, supporting variable resolutions, and excelling at visual tasks like OCR and chart understanding. Additionally, the E2B and E4B models feature native audio input for speech recognition and understanding.
- Longer context: Process long-form content seamlessly. The edge models feature a 128K context window, while the larger models offer up to 256K, allowing you to pass repositories or long documents in a single prompt.
- 140+ languages: Natively trained on over 140 languages, Gemma 4 helps developers build inclusive, high-performance applications for a global audience.
More details can be found in this blog post, while the importance of open sourcing is addressed here.
