Google DeepMind released Gemma 4 in March 2026 as a family of open models spanning small on-device variants through larger server-oriented models. The family includes E2B, E4B, 12B, 26B A4B, and 31B sizes, with multimodal capabilities and context windows of up to 256K tokens depending on the model.
Whether you’re a developer building the next generation of AI-powered apps, a researcher pushing the boundaries of machine learning, or a business looking to deploy capable AI on your own infrastructure, Gemma 4 has something for everyone.
What Is Gemma 4?
Gemma is Google DeepMind’s family of open models with published weights. Gemma 4 is available in pre-trained and instruction-tuned variants under an Apache 2.0 license, according to Google’s model documentation. The exact deployment experience depends on the model size, quantization, inference stack, and available hardware.
All Gemma 4 models support text and image input. Audio is supported natively on E2B, E4B, and 12B models, while video is processed as sequences of frames. Google documents context windows of 128K for E2B/E4B and 256K for 12B, 26B A4B, and 31B.
Four Model Sizes for Every Use Case
One of the most exciting aspects of Gemma 4 is the sheer variety of sizes on offer, each designed for a specific deployment environment:
E2B & E4B — Built for Mobile and Edge
The E2B (2.3B effective parameters) and E4B (4.5B effective parameters) are compact powerhouses built for on-device deployment. The “E” stands for effective parameters — a design choice made possible by Per-Layer Embeddings (PLE), a technique that gives each decoder layer its own small embedding per token, maximising intelligence without increasing compute costs.
These models support text, image, and audio input, making them ideal for real-time applications on phones, IoT devices, Raspberry Pi, and NVIDIA Jetson Nano. They can run completely offline with near-zero latency — a game changer for privacy-conscious deployments and edge computing scenarios. Each E-series model features a 128K token context window.
26B A4B — The Efficiency Champion
The 26B A4B uses a Mixture-of-Experts (MoE) architecture with 25.2 billion total parameters, but only activates 3.8 billion during inference. The “A” stands for active parameters. This means it runs at roughly the speed of a 4B model while delivering intelligence that rivals much larger dense models.
With a 256K token context window, support for text and images, and 128 total experts (8 active at a time), the 26B A4B is the go-to choice for developers who need maximum performance per compute dollar.
31B Dense — The Flagship
The 31B Dense model is Gemma 4’s most capable offering, featuring 30.7 billion parameters, a 256K token context window, and a powerful ~550M parameter vision encoder. Designed for workstations, consumer GPUs, and server deployments, this is the model to reach for when you need the best possible results on complex reasoning, coding, and multimodal tasks.
Groundbreaking Capabilities with Gemma 4
🧠 Built-In Reasoning (Thinking Mode)
All Gemma 4 models include a configurable thinking mode that allows the model to reason step-by-step before producing a final answer. Enable it with a single token (<|think|>) in your system prompt — no additional infrastructure required.
🖼️ Advanced Multimodal Understanding
Gemma 4 handles a rich variety of inputs:
- Images at variable aspect ratios and resolutions, with a configurable visual token budget (from 70 to 1120 tokens)
- Video by processing sequences of frames (up to 60 seconds at 1 fps)
- Audio on E2B and E4B models — including automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages (up to 30 seconds)
- Documents and PDFs, charts, handwriting, on-screen UI elements, and OCR in multiple languages
🌍 Multilingual at Scale
Gemma 4 offers out-of-the-box support for 35+ languages, with pre-training conducted on data spanning over 140 languages. This makes it one of the most linguistically capable open models available.
💻 Coding & Agentic Workflows
Gemma 4 marks a major step up in coding ability. The 31B model achieves a Codeforces ELO of 2150 — a competitive programming score that places it among the top tier of AI coding assistants. Native function-calling support enables developers to build autonomous agents that can plan, use tools, and complete multi-step tasks with minimal human intervention.
📜 Extended Context Windows
With context windows of 128K tokens (E2B/E4B) and 256K tokens (26B A4B/31B), Gemma 4 can process entire codebases, lengthy research papers, long video transcripts, and complex multi-turn conversations without losing track of earlier content.
Benchmark Performance: The Numbers Speak
Here’s how Gemma 4 compares across key benchmarks (instruction-tuned models):
| Benchmark | Gemma 4 31B | Gemma 4 26B A4B | Gemma 4 E4B | Gemma 3 27B |
|---|---|---|---|---|
| MMLU Pro | 85.2% | 82.6% | 69.4% | 67.6% |
| AIME 2026 (no tools) | 89.2% | 88.3% | 42.5% | 20.8% |
| LiveCodeBench v6 | 80.0% | 77.1% | 52.0% | 29.1% |
| Codeforces ELO | 2150 | 1718 | 940 | 110 |
| GPQA Diamond | 84.3% | 82.3% | 58.6% | 42.4% |
| MMMU Pro (Vision) | 76.9% | 73.8% | 52.6% | 49.7% |
| MMMLU (Multilingual) | 88.4% | 86.3% | 76.6% | 70.7% |
The improvements over Gemma 3 are dramatic across the board — particularly in coding and mathematical reasoning, where the 31B model scores more than four times higher than its predecessor on competitive programming benchmarks.
Smart Architecture Under the Hood
Gemma 4 uses a hybrid attention mechanism that interleaves local sliding window attention with full global attention. This design delivers the processing speed and low memory footprint of a lightweight architecture while retaining the deep contextual awareness needed for long, complex tasks.
For memory efficiency on long contexts, global attention layers use unified Keys and Values alongside Proportional RoPE (p-RoPE) — a technique that helps the model handle extended sequences without a ballooning memory footprint.
Safety and Responsible AI
Google DeepMind has subjected Gemma 4 to the same rigorous safety evaluations applied to its proprietary Gemini models. The results show major improvements in content safety across all categories compared to Gemma 3 — including reduced policy violations and fewer unjustified refusals.
Training data was carefully filtered for sensitive personal information, CSAM, and low-quality or harmful content. The models are released alongside Google’s Responsible Generative AI Toolkit and full usage guidelines.
How to Get Started
Gemma 4 is available to download and deploy right now across all major platforms:
Download model weights:
Train and deploy:
- Keras, PyTorch, JAX, Hugging Face Transformers
- Google Cloud Vertex AI, GKE, Cloud Run
- Edge deployment via Google AI Edge, MediaPipe, and gemma.cpp
Try it immediately: Google AI Studio lets you test the 31B model right in your browser — no setup required.
Quick Start Code (Hugging Face Transformers)
import torch
from transformers import AutoProcessor, AutoModelForCausalLM
MODEL_ID = "google/gemma-4-E2B-it"
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum entanglement simply."},
]
text = processor.apply_chat_template(
messages, tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
Why Gemma 4 Matters
Open AI models have historically lagged behind their proprietary counterparts — but Gemma 4 closes that gap substantially. Here’s why it matters:
For developers: You get frontier-level capabilities with full control over your data and infrastructure. No API rate limits, no per-token costs at inference time, and the freedom to fine-tune for your specific use case.
For enterprises: Gemma 4 undergoes the same security protocols as Google’s proprietary models, making it a trustworthy foundation for sensitive or regulated deployments. Run it on your own cloud or on-premises without sending data to third-party servers.
For researchers: The Apache 2.0 license means you can study, modify, and build on Gemma 4 freely — and the model card provides unusually detailed transparency about architecture, training data, and evaluation methodology.
For the AI ecosystem: When powerful open models exist, innovation accelerates. Gemma 4 democratises access to state-of-the-art AI in a way that benefits students, startups, and researchers globally.
Final Thoughts
Gemma 4 provides several deployment options across mobile, edge, laptop, workstation, and server environments. The right choice depends on the balance you need between model capability, memory, latency, modality support, and infrastructure cost.
For developers, a useful evaluation process is to choose the smallest Gemma 4 variant that meets the quality requirements of the application, then measure memory use, latency, accuracy, and tool behavior on representative tasks.
The future of open AI just got a lot more capable. And it fits in your pocket.
📚 Resources: – Gemma 4 Model Card – Gemma 4 on Google DeepMind – Gemma Documentation – Try Gemma 4 in Google AI Studio
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