In April 2026, Google DeepMind released Gemma 4 — the latest generation of its open-source AI model family. Built on the same research foundation as the Gemini models, Gemma 4 represents a significant leap forward for open-source AI: true multimodal understanding, a 256K token context window, built-in reasoning capabilities, and support for over 140 languages. All released under the Apache 2.0 license.
This guide covers everything you need to know about Gemma 4 — what it is, what models are available, what makes it special, and how to get started.
The Gemma 4 Model Family
Gemma 4 is not a single model but a family of five models, each designed for different use cases and hardware constraints:
Gemma 4 E2B is the smallest model in the family, using a Mixture-of-Experts (MoE) architecture with 2 billion active parameters out of 4 billion total. Despite its compact size — just 3.2 GB with Q4 quantization — E2B delivers impressive capabilities for on-device deployment. It runs comfortably on smartphones and edge devices, making private, offline AI accessible to everyone.
Gemma 4 E4B scales up the MoE approach with 4 billion active parameters out of 8 billion total. It adds full multimodal support including audio understanding, making it the sweet spot for developers who need strong capabilities without heavy infrastructure. E4B processes text, images, video, and audio natively.
Gemma 4 12B joined the family on June 3, 2026, after the original April launch. It uses a unified multimodal architecture rather than attaching separate encoders to a text backbone, which makes it the first variant to consider when a single model must handle several input types without a hand-built routing layer. Because it shipped later than the rest of the lineup, published parameter, context, and memory figures are still settling — check the official model card on Hugging Face before sizing hardware around it.
Gemma 4 26B A4B is the production workhorse. With 9 billion active parameters out of 26 billion total via its MoE architecture, it delivers performance that rivals much larger models while keeping inference costs manageable. It excels at code generation (80% on LiveCodeBench), agentic tool use, and complex reasoning tasks.
Gemma 4 31B Dense is the flagship model. Unlike the others, it uses a traditional dense architecture where all 31 billion parameters are active during inference. This gives it the highest raw quality across benchmarks, particularly for long-context understanding and nuanced multilingual tasks. It is the best choice when quality is the top priority.
Key Features
Multimodal Understanding
Gemma 4 models natively process text, images, video, and audio (audio support in E2B and E4B). Variable resolution image input allows the models to analyze images at their native resolution without forced resizing, improving accuracy on visual tasks like chart reading, document OCR, and image analysis.
256K Token Context Window
All Gemma 4 models support a 256K token context window — enough to process entire codebases, long legal documents, book-length texts, or extended multi-turn conversations. This is a substantial improvement over previous generations and puts Gemma 4 on par with the largest proprietary models in terms of context length.
Built-in Reasoning
Gemma 4 includes enhanced thinking capabilities that allow it to break down complex problems step by step. This is particularly valuable for mathematical reasoning, code debugging, and multi-step planning tasks. The reasoning mode can be toggled on or off depending on the use case.
Native Function Calling
For developers building agents and tool-using applications, Gemma 4 supports native function calling with structured JSON output. This means the model can reliably invoke external APIs, query databases, and orchestrate multi-step workflows without brittle prompt engineering.
140+ Language Support
Gemma 4 supports over 140 languages with strong performance across the board. On the Massive Multitask Multilingual Understanding (MMMLU) benchmark, it scores 85.2% — making it one of the most capable multilingual open-source models available. This enables truly global applications in translation, content moderation, and customer support.
Benchmarks
Gemma 4 delivers competitive or leading results across major benchmarks:
- LiveCodeBench: 80% (26B A4B) — strong code generation and understanding
- MMMLU: 85.2% — exceptional multilingual comprehension
- MMMU: 72.4% (31B Dense) — strong multimodal understanding
- MATH-500: 91.8% (31B Dense) — near-expert mathematical reasoning
- Arena Hard: 85.1% (31B Dense) — competitive conversational ability
These numbers place Gemma 4 among the top open-source models and competitive with many proprietary alternatives.
Getting Started
The easiest way to try Gemma 4 is through Ollama:
ollama pull gemma4:26b
ollama run gemma4:26bFor browser-based experimentation, visit Google AI Studio where you can chat with Gemma 4 and get an API key at no cost.
For production deployment, models are available on HuggingFace, Kaggle, and can be served with frameworks like vLLM, TGI, and SGLang.
Check out our Getting Started Guide for detailed setup instructions and our Downloads page for all available platforms.
Conclusion
Gemma 4 marks a turning point for open-source AI. For the first time, developers have access to a model family that combines true multimodal understanding, long context, built-in reasoning, native tool use, and broad multilingual support — all under an Apache 2.0 license with no usage restrictions.
Whether you are building a code assistant, deploying AI on a smartphone, processing thousands of documents, or creating multilingual customer support agents, Gemma 4 provides a capable, efficient, and truly open foundation to build on. The gap between open-source and proprietary AI has never been smaller.
