Google Gemma 4 - Google's most intelligent open models to date
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Gemma 4 is Google DeepMind’s most capable open model family, delivering advanced reasoning, multimodal processing, and agentic workflows. Optimized for everything from mobile devices to GPUs, it enables developers to build powerful AI apps efficiently with high performance and low compute overhead.
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Google's Gemma 4 looks like a serious leap forward in open AI models.
An open model family built for advanced reasoning and agentic workflows, it solves a key problem: getting frontier-level intelligence without massive compute costs or closed ecosystems.
Stands out for its intelligence-per-parameter — outperforming models up to 20x larger while running efficiently on phones, laptops, and desktops.
Key Features:
Advanced reasoning – Strong multi-step planning, math, and instruction-following
Agentic workflows – Native function calling, structured JSON output, and system instructions
Multimodal capabilities – Supports images, video, and audio inputs
Long context window – Up to 256K tokens for handling large documents and codebases
Code generation – High-quality offline coding and local AI assistants
140+ languages – Built for global, multilingual applications
Hardware efficiency – Runs across mobile devices, laptops, and GPUs
It’s open (Apache 2.0), meaning developers get full control, flexibility, and the ability to run and fine-tune locally or in the cloud.
Start experimenting with Gemma 4 now in @Google AI Studio 2.0 or download the model weights from:
Ollama
Kaggle
LM Studio
Docker
Hugging Face
Who's it for? developers, startups, and enterprises building AI agents, coding assistants, multimodal apps, or privacy-first solutions.
Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation.
Read more here:
https://deepmind.google/models/gemma/gemma-4/
https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/
https://opensource.googleblog.com/2026/03/gemma-4-expanding-the-gemmaverse-with-apache-20.html
Just posted about this on X today. Apache 2.0, runs on your own hardware, 256K context window. The fact that you can run this locally on a laptop and still get serious reasoning is wild. I'm curious how the Flutter/Dart code generation compares to the bigger closed models since that's most of what I write these days.
curious about the "low compute overhead" claim - are you seeing meaningful performance gains over Llama models in the same parameter range? we're always evaluating new models for healthcare applications where inference speed matters a lot.