Google DeepMind released Gemma 4 in April 2026 as a fully open-source AI model family under the Apache 2.0 license. The lineup includes the 31B Dense model — ranked 3rd among open models on Arena AI — all the way down to the E2B model that runs on smartphones. Four distinct sizes give developers unprecedented flexibility.
This guide walks you through how to leverage Gemma 4 within the Antigravity development environment to supercharge your app development workflow. If you've heard about Gemma 4 but aren't sure where to start, this is the article for you.
What Is Gemma 4 — The Essentials for Developers
Gemma 4 is built on the same technology foundation as Gemini 3, making it Google DeepMind's most capable open model family to date. Here's what sets it apart from previous Gemma releases:
- Multimodal capabilities: Processes both text and images across all sizes, with audio support on edge models
- 256K token long context: Handles massive codebases and documentation in a single pass
- Apache 2.0 license: Completely free for commercial use, modification, and redistribution
- Four model sizes: Choose the right model for your specific use case
The four models break down as follows:
- E2B (~2.3B effective parameters): Edge-optimized for smartphones and Raspberry Pi. Supports audio and image input
- E4B (~4.5B effective parameters): Higher accuracy edge model with function calling support
- 26B MoE (~3.8B active parameters): Mixture of Experts architecture that activates only the parameters needed during inference. Best balance of speed and quality
- 31B Dense (all parameters active): Maximum inference quality. Ranked 3rd on the Arena AI open model leaderboard
Why Antigravity + Gemma 4?
Antigravity is Google's AI-powered integrated development environment with native Gemini integration. Adding Gemma 4 to the mix opens up possibilities that cloud-only APIs can't match.
The biggest advantage is local AI development without cloud API dependency. Gemma 4 runs directly on your local machine or edge devices, so AI features work even without an internet connection. User data never leaves the device, making it ideal for privacy-focused app development.
Combined with Antigravity's agent capabilities, you get AI assistance across the entire development workflow — from code generation through testing and debugging.
Coming from Gemma 3 — What Actually Changes
If you were already running indie tools on Gemma 3, you stand to gain the most from Gemma 4. Rather than reshuffling spec numbers, it helps to look at how the day-to-day feel of building changes.
The biggest shift is that image input is built in from the start. Gemma 3 was centered on text, so handling images meant bolting a separate vision model alongside it. With Gemma 4 you can try features like reading receipts, captioning photos, or checking images on-device without adding another model. The edge models handle audio too, so your input options widen on their own.
The jump to a 256K-token context is quiet but useful. You can hand over a mid-sized codebase without splitting it, which cuts down the time spent deciding where to cut.
The new 26B MoE option is easy to overlook. It adds a well-judged middle gear between the smartphone-class edge models and the heaviest 31B Dense — the one you reach for when the edge model feels thin but 31B is heavier than you need.
The migration itself is low-friction. The Apache 2.0 license carries over, and your prompt style mostly transfers as-is. Still, a prompt you tuned for Gemma 3 won't necessarily behave identically. I would run it through your own evaluation set once and compare outputs before shipping.
In my own indie development, I lean on a local Gemma-class model for drafts and rough passes, keeping my code and data on the machine, and only send the steps that truly need top quality to cloud Gemini. I keep the same split when running the Dolice Labs blogs — shape it locally first, and reach outside only for the final polish.
Setting Up Your Development Environment
Here's how to get Gemma 4 running in your Antigravity workflow.
Step 1: Download the Gemma 4 Model
Start by downloading the model from Google AI Studio or Hugging Face.
# Assumes google-genai is installed via pip
from google import genai
# Using Gemma 4 through Google AI Studio
client = genai.Client(api_key="YOUR_API_KEY")
# Generate content with Gemma 4
response = client.models.generate_content(
model="gemma-4-27b-it", # 26B MoE instruction-tuned variant
contents="Write a Flutter login screen implementation"
)
print(response.text)
# Output: Flutter login screen code (Material Design compliant)Step 2: Integrate with Your Antigravity Project
With your project open in Antigravity, configure Gemma 4 for local execution.
# Running Gemma 4 locally via Ollama
ollama pull gemma4:26b
# Verify the model works
ollama run gemma4:26b "Hello, Gemma 4!"
# Output: Hello! I'm Gemma 4, a large language model...Step 3: Connect with Antigravity Agents
Use Antigravity's agents.md file to integrate Gemma 4 into your development workflow.
<!-- Example .antigravity/agents.md configuration -->
# Code Review Agent
This agent uses Gemma 4 for automated code review.
## Steps
1. Retrieve the diff of changed files
2. Send to Gemma 4 for quality and security review
3. Output findings as inline commentsChoosing the Right Model for Your Use Case
Each of Gemma 4's four models is optimized for different development scenarios.
Building Mobile Apps → E2B or E4B
When you want AI running directly on your users' phones, edge models are the way to go. E2B operates with roughly 2GB of memory, while E4B delivers higher accuracy responses.
# Example: Embedding in an Android app with LiteRT-LM
# Add to build.gradle:
# implementation 'com.google.ai.edge:litert-lm:1.0.0'
# Kotlin model initialization
"""
val model = LiteRtLm.create(
context = applicationContext,
modelPath = "gemma-4-e2b.task" // placed in assets folder
)
val response = model.generateContent("Answer the user's question")
println(response.text)
"""Web App Backend → 26B MoE or 31B Dense
For server-side AI processing, the larger models shine. The 26B MoE model offers an excellent balance between inference speed and quality, making it perfect for API servers.
# Building a Gemma 4 API server with FastAPI + Ollama
from fastapi import FastAPI
import httpx
app = FastAPI()
@app.post("/api/generate")
async def generate(prompt: str):
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:11434/api/generate",
json={
"model": "gemma4:26b",
"prompt": prompt,
"stream": False
}
)
return response.json()
# Run: uvicorn main:app --reload
# Test: curl -X POST "http://localhost:8000/api/generate?prompt=Hello"Code Generation and Review → 31B Dense
For the highest quality code generation and review, the 31B Dense model is your best bet. It scored 80.0% on LiveCodeBench v6, handling complex logic implementations with ease.
Looking back
Gemma 4 is an incredibly compelling open model for developers. The freedom of the Apache 2.0 license, four model sizes covering everything from smartphones to servers, and Gemini 3-caliber performance — combine all of this with Antigravity's development environment, and AI-powered app development becomes remarkably smooth.
Start by downloading the E2B or E4B model and running it locally. Once you connect it with Antigravity's agent features, you'll experience an AI-assisted workflow that supports you from coding through testing and deployment.
If you're new to Antigravity, check out the Antigravity Complete Beginner's Guide 2026 first. For multi-model strategies, Antigravity Multi-Model Mastery — Switching Between Gemini, Claude, and GPT is an excellent resource.