Gemma 4 Ships Under Apache 2.0 and the Numbers Are Absurd

Google released Gemma 4 on April 2 with four model variants: E2B, E4B, 26B MoE, and 31B Dense. The license change is the first thing worth noting. Every previous Gemma had restrictions that made lawyers nervous. Gemma 4 is Apache 2.0. Full stop. Use it in any product, any way you want, no strings.

Now the performance numbers. Compared to Gemma 3, the 31B dense model jumped AIME math from 20.8% to 89.2%. Coding went from 29.1% to 80.0%. Science from 42.4% to 84.3%. These aren't incremental improvements. This is a different class of model wearing the same name. The 31B dense variant beats models with 10x the parameter count on multiple benchmarks, and it runs on a single consumer GPU.

All four variants handle text, vision, and audio natively with 256K context and 140+ language support. The E2B and E4B variants are small enough for edge deployment. llama.cpp already merged Qwen3-Omni and audio support alongside Gemma 4, meaning you can run multimodal inference locally today. Simon Willison published a recipe for local audio transcription using Gemma 4 E2B via MLX on Apple Silicon with a single uv command.

What this means practically: if you're building a product that needs an open model, Gemma 4 just became the default recommendation. The Apache 2.0 license removes the last barrier. The performance gap between open and proprietary models has collapsed for most practical tasks. I still use Claude for complex agentic work where reasoning depth matters, but for inference endpoints in production apps? For edge deployment on devices? For anything where you need a model you fully control? Gemma 4 changes the conversation.

The small MoE variants are particularly interesting. r/LocalLLaMA is converging on ~3B active / 30-35B total as the new standard weight class for consumer hardware, the way 7B dense models became standard two years ago. Gemma 4 E4B fits right in that sweet spot.