On April 2, 2026, Google released Gemma 4. The most capable open-weight AI model family they have ever shipped. Free. Apache 2.0 licensed. Runs on your phone, your laptop, a Raspberry Pi or an enterprise server.
Developers have downloaded Gemma models over 400 million times since the first release. Gemma 4 is what happens when Google actually listened to what those developers asked for next.
Here is everything you need to know.
What Is Gemma 4?
Gemma is Google's family of open-weight AI models. Think of it as the open-source sibling of Gemini. Same underlying research. Same world-class training infrastructure. But instead of being locked behind an API, Gemma gives you the actual model weights. You download them, you run them, you own the experience completely.
Gemma 4 is the fourth generation of this family and it is a significant step up from everything before it.
Four Models for Every Hardware Level
Gemma 4 comes in four sizes.
E2B runs on smartphones. 2.3 billion effective parameters. 4x faster than the previous version. 60 percent less battery. This is the foundation for Gemini Nano 4 on Android.
E4B is the stronger edge model at 4.5 billion effective parameters. Both edge models support a 128K context window.
26B MoE activates only 3.8 billion parameters during inference despite its 26 billion total. Fast, efficient, runs on consumer GPUs. 256K context window.
31B Dense is the flagship. Currently ranked third on the Arena AI open model leaderboard. Best for fine-tuning and complex tasks. Also 256K context.
What Makes It Different
Multimodal natively. All variants understand text, images and audio together. No separate product. No extra cost.
256K context window. Pass in an entire codebase or a long document in a single prompt. Locally.
Thinking mode built in. Chain-of-thought reasoning and tool calling are both strengthened. Suitable for agentic workflows that run completely offline.
140 languages natively trained. Not translated. Actually trained on them.
Apache 2.0 license. This is the biggest change from previous Gemma versions. You can build commercial products with it, modify it, redistribute it and keep everything private. No royalties. No data going to Google. No restrictions.
The Benchmark Jump
The performance improvement over Gemma 3 is not incremental.
AIME 2026 math benchmark: 20.8 percent to 89.2 percent.
LiveCodeBench coding: 29.1 percent to 80.0 percent.
GPQA science: 42.4 percent to 84.3 percent.
That is a fundamentally different model.
Where to Try It Right Now
Google AI Studio (browser, no setup): aistudio.google.com
Hugging Face (all weights): huggingface.co/google/gemma-4
Ollama (local, one command): ollama run gemma4
Kaggle for free GPU experimentation. Vertex AI for fine-tuning and enterprise deployment.
Who Should Pay Attention
Android developers especially. Gemma 4 is the base model for Gemini Nano 4 which will ship to hundreds of millions of Android devices later this year. Code written for Gemma 4 today will work on those devices automatically.
Anyone building privacy-sensitive applications in healthcare, finance or government now has a world-class model they can run fully on-premise.
Anyone currently paying for API access to handle straightforward tasks should test whether the 26B model covers their workload locally. For many use cases it will, and the API cost disappears.
The Honest Part
The 31B model needs serious hardware. An 80GB H100 for the full version or a high-end consumer GPU for quantized. If you do not have that, the 26B MoE is the more practical local option.
The edge models trade some reasoning depth for speed. For complex tasks the larger models will produce noticeably better results.
Video input requires extracting frames. Native video is not supported yet.
The Bottom Line
A world-class multimodal reasoning model. Free to use commercially. Runs on hardware you already own. No API dependency. No data leaving your machine.
That is worth taking seriously.
Published by RentPrompts
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