The AI world is buzzing with the release of Gemma 4, Google’s latest “open-weight” model family. Designed for high performance on local hardware, this new architecture is built using the same world-class technology as Gemini 3. Whether you are a mobile developer, a data scientist, or an AI enthusiast, these models bring frontier-level intelligence directly to your workstation or smartphone.
What’s New in the Latest Release?
The software isn’t just a minor update; it’s a complete overhaul focused on Agentic Workflows—meaning the models can now use tools, call APIs, and execute multi-step plans autonomously.
- Four Versatile Sizes: From the mobile-friendly E2B to the powerhouse 31B Dense version.
- Multimodal Capabilities: Native support for Vision (images/video) across all variants, and native Audio processing in the edge models.
- Massive Context Window: Up to 256K tokens for the larger versions, allowing you to process entire codebases or long documents in one go.
- Global Reach: Natively trained in over 140+ languages, making it one of the most linguistically capable open models available.
Model Lineup & Specifications
Google has optimized the Gemma 4 family for different hardware constraints, ensuring efficiency across the board:
| Model Variant | Parameters | Best For | Context Window |
| E2B | 2 Billion | Ultra-fast mobile & IoT devices (3x faster than E4B). | 128K |
| E4B | 4 Billion | High-reasoning tasks on Android/Edge devices. | 128K |
| 26B (MoE) | 26 Billion | High speed with 3.8B active params; ideal for low-latency agents. | 256K |
| 31B Dense | 31 Billion | Maximum raw quality, deep logic, and complex coding tasks. | 256K |
Performance Benchmarks: How Does it Rank?
This generation is punching way above its weight class. According to the latest Arena AI Text Leaderboard (April 2026), the 31B Dense model currently ranks as the #3 open model in the world, outperforming models 20 times its size.
- Reasoning: Significant jumps in GSM8K (Math) and HumanEval (Coding).
- Efficiency: The 26B MoE variant provides the knowledge of a large model with the inference speed of a small one.
- Vs Qwen 3.5: Early tests suggest Google’s release leads in logic and instruction following, while Qwen 3.5 remains a strong competitor in specific tool-calling benchmarks.
How to Use and Download AI Models
The entire family is released under the Apache 2.0 License, making it free for commercial use without the heavy restrictions of previous versions.
- Hugging Face: Access the full weights, GGUF quants, and AWQ versions on the official repository.
- Ollama: Run the models locally with a single command:
ollama run gemma4:31borollama run gemma4:e4bfor edge testing. - Google Cloud: Deploy at scale using NVIDIA Blackwell GPUs for maximum throughput.
- Unsloth: For those looking to fine-tune, Unsloth has already released kernels that make training 2x faster with 70% less memory.
Key Features for Developers
- Native Tool Use: High-quality function calling and structured JSON outputs.
- Coding Performance: Exceptional at offline code generation, rivaling Claude 3.5 Sonnet in local environments.
- Edge Optimization: Collaboration with Qualcomm and MediaTek ensures near-zero latency on the latest mobile chips.
Note to Reader: If you are using Ollama, ensure you are on version 0.20 or higher to support the new Gemma 4 architecture and thinking tokens!