What’s new in Gemma 4

By | May 11, 2026

Gemma 4 is designed with privacy and security at its core, allowing users to handle sensitive tasks without compromising their data. Here is how it manages privacy:

  • Local Execution: Gemma 4 models are built to run directly on the hardware you own, including phoneslaptops, and desktops (0:330:36). This enables you to perform state-of-the-art reasoning and coding pipelines locally, without the need to upload your data outside of your controlled environment (1:201:26).
  • Rigorous Security Standards: Despite being open models, they are developed by Google DeepMind and undergo the same rigorous security protocols as proprietary models, providing a trusted foundation for both developers and enterprise infrastructure (2:252:34).
  • Open Licensing: By releasing these models under an Apache 2.0 license, users have greater transparency and control over the technology, which further aids in building secure and private systems (0:400:43).

Google launches Gemma 4, calls it the most intelligent open model: What is  it and how to use – Firstpost

Released in April 2026, Gemma 4 is the latest generation of Google’s open-weight models. It marks a significant shift from previous versions by moving beyond text-only interaction into a fully native multimodal and agentic framework.

 

Built on the same research as Gemini 3, Gemma 4 is designed to provide “frontier-level intelligence” on local hardware and across the cloud.

 

1. Key Model Sizes

The family is structured into four main sizes to balance performance with hardware constraints:

 

  • E2B & E4B (Effective 2B/4B): Designed for mobile, IoT, and edge devices (like Raspberry Pi 5). They use Per-Layer Embeddings (PLE) to maintain high quality with a small memory footprint.

     

  • 26B A4B (MoE): A Mixture-of-Experts model that has 25.2B total parameters but only activates 3.8B during inference, offering high speed similar to the 4B model but with the reasoning of a much larger one.

     

  • 31B Dense: The flagship model for raw quality, ranking among the top open models globally for complex logic and fine-tuning.

     

2. Native Multimodality

Unlike earlier versions that were text-focused, Gemma 4 features native vision and audio processing built directly into the architecture:

 

  • Vision: Can handle variable aspect ratios and resolutions for tasks like OCR, chart understanding, and UI analysis.

     

  • Audio: Supports spoken language processing without needing a separate speech-to-text model. This allows for direct transcription, translation, and audio-based Q&A (available on the E2B and E4B models).

     

  • Video: Capable of understanding video sequences and generating captions or answering questions about the content.

     

3. Agentic & Coding Capabilities

Gemma 4 is “purpose-built” for autonomous workflows:

 

  • Agentic Workflows: Includes native support for function calling, structured JSON output, and native system instructions.

     

  • Reasoning: Features a new “Thinking Mode” (triggered by a <|think|> token) that allows the model to output internal reasoning before providing a final answer.

  • Coding: Significant improvements in offline code generation, making it a powerful local-first programming assistant.

4. Performance & Efficiency from What’s new in Gemma 4

  • Context Window: Up to 256K tokens for the larger models (128K for edge models), allowing for the processing of entire code repositories or long documents.

     

  • Speed: Introduction of Multi-Token Prediction (MTP) drafters (speculative decoding) provides up to a 3x speedup by predicting multiple future tokens simultaneously.

  • Language Support: Natively trained in over 140 languages.

5. Open Licensing & Access

Gemma 4 remains under the commercially permissive Apache 2.0 license, meaning it can be used for commercial products without royalty obligations. It is available through standard platforms like Hugging Face, Ollama, and Google Cloud (Vertex AI, GKE, and Cloud Run).

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