Synopsis
The Gemma 4 model offers capabilities such as advanced reasoning, agentic workflows, coding, and support for over 140 languages. The models are also capable of solving complex mathematical problems and generating high-quality code, positioning them as potential local AI coding assistants.Google described Gemma 4 as its “most capable open model” to date. The company has released it in four variants, each designed for different levels of performance and hardware requirements including Effective 2B (E2B), Effective 4B (E4B), 26B Mixture of Experts (MoE) and 31B Dense.
Gemma 4 specifications
Gemma 4 variant Effective 2B (E2B) is a compact model with around 2 billion parameters, Effective 4B (E4B) is a slightly larger version with improved capability, 26B Mixture of Experts (MoE), a model using a specialised architecture and 31B Dense is the largest and most powerful version in the lineup, the company blog post read.
It added that the 31B dense model ranks among the top-performing open models on widely used industry benchmarks.
Parameters refer to the number of adjustable values in a model; generally, more parameters allow for better performance but require more computing power.
The Mixture of Experts (MoE) architecture is a technique where only a subset of the model’s components are activated for each task. This improves efficiency by reducing the amount of computation needed compared to traditional “dense” models, where all parameters are used every time.
Gemma 4 features
The Gemma 4 model offers capabilities such as advanced reasoning, agentic workflows, coding, and support for over 140 languages. The models are also capable of solving complex mathematical problems and generating high-quality code, positioning them as potential local AI coding assistants.
A key emphasis of Gemma 4 is efficiency. Smaller and optimised models allow developers to run advanced AI systems on more modest hardware, including personal workstations or edge devices, rather than requiring large data centers.
This approach is intended to make “frontier-level” AI capabilities more accessible to a broader range of developers, the company mentioned.
