Google Gemma 4: Everything You Need to Know About the Most Capable Open AI Model

What it is: Gemma 4 is Google DeepMind’s newest family of open-source AI models — purpose-built for advanced reasoning, agentic workflows, and on-device AI
Who it’s for: Developers, researchers, and anyone interested in running powerful AI locally or building AI-powered applications
Best if: You want frontier-level AI capabilities without paying for API access — or you want to run AI completely offline on your own hardware
Skip if: You just want to use AI through an app — try Gemini instead, which is Google’s consumer-facing product built on similar technology

Table of Contents

Key Takeaways

Gemma 4 is the most capable open-source AI model family available. It ranks #3 among all open models on the Arena AI leaderboard — outperforming models 20x its size.
Four model sizes cover everything from phones to servers: E2B (2.3B params), E4B (4.5B params), 26B MoE (3.8B active), and 31B Dense.
Massive benchmark jumps over Gemma 3: math scores went from 20.8% to 89.2%, coding from 29.1% to 80.0%, and science from 42.4% to 84.3%.
Multimodal from day one — all models handle text, images, and video natively. The smaller E2B and E4B models also understand audio.
Apache 2.0 license — fully open source. You can use it commercially, modify it, fine-tune it, and deploy it anywhere with no restrictions.
Runs on your hardware. The smaller models work on phones and Raspberry Pi. The larger ones run on consumer GPUs. No cloud required.

What Is Gemma 4?

Gemma is Google DeepMind’s family of open-source AI models. Think of it as the open counterpart to Gemini — built from the same research and technology, but released publicly so anyone can download, modify, and run the models on their own hardware.

Gemma 4, released on April 2, 2026, is the biggest update in the family’s history. Google describes it as delivering “an unprecedented level of intelligence-per-parameter” — meaning these models punch far above their weight compared to the amount of computing power they require.

Since the first Gemma launch, developers have downloaded Gemma models over 400 million times, creating more than 100,000 variants in what Google calls the “Gemmaverse.” Gemma 4 is the culmination of that community momentum combined with Google’s latest AI research.

The Four Gemma 4 Models Explained

Gemma 4 comes in four sizes, each designed for different hardware and use cases:

Gemma 4 on the Arena AI leaderboard — the 31B model ranks #3 among all open models, outperforming far larger competitors. (Source: Google)

Gemma 4 E2B — The Smallest (2.3 Billion Parameters)

Engineered for phones, IoT devices, and Raspberry Pi. Runs completely offline with near-zero latency. Supports text, images, video, and native audio input. 128K context window. This is the model that will power on-device AI in future Android phones.

Gemma 4 E4B — The Mobile Powerhouse (4.5 Billion Parameters)

Same capabilities as E2B but with more reasoning power. Still fits on mobile devices and edge hardware. Supports all modalities including audio. 128K context window. The sweet spot for mobile app developers who need more intelligence without leaving the device.

Gemma 4 26B MoE — The Speed Demon (26B Total, 3.8B Active)

A Mixture of Experts model that activates only 3.8 billion of its 26 billion total parameters during any single query. This makes it exceptionally fast — you get the intelligence of a much larger model at the speed of a small one. Ranks #6 on the Arena AI open model leaderboard. 256K context window. Scored 88.3% on the AIME 2026 math benchmark with only 3.8B active parameters — one of the most parameter-efficient reasoning models ever built.

Gemma 4 31B Dense — The Quality King (31 Billion Parameters)

The largest and most capable model in the family. Every parameter is active on every query, maximizing raw output quality. Ranks #3 on the Arena AI open model leaderboard — outperforming models with 20x more parameters. 256K context window. Best foundation for fine-tuning. Runs on a single NVIDIA H100 GPU unquantized, or on consumer GPUs with quantization.

Benchmark Performance: Gemma 4 vs Gemma 3

The improvements from Gemma 3 to Gemma 4 are not incremental — they are generational leaps:

Benchmark	Gemma 3 (27B)	Gemma 4 (31B)	Improvement
AIME 2026 (Math)	20.8%	89.2%	+329%
LiveCodeBench (Coding)	29.1%	80.0%	+175%
GPQA (Science)	42.4%	84.3%	+99%
MMLU Pro (General)	—	85.2%	—
Codeforces ELO	110	2150	+1855%

The math and coding improvements are especially striking. A Codeforces ELO jump from 110 to 2150 means Gemma 4 went from below-beginner to expert-level competitive programming. For context, that puts it in the range of experienced human competitive programmers.

Official Gemma 4 benchmark results across model sizes. (Source: Google)

What Can Gemma 4 Actually Do?

Here is what the models are designed for, in plain English:

Google’s official Gemma 4 overview video.

Advanced Reasoning

Multi-step planning, complex logic, and deep analysis. Gemma 4 can break down a problem, work through it methodically, and explain its reasoning. This is the core improvement over previous versions.

Agentic Workflows

Native support for function-calling, structured JSON output, and system instructions. In practical terms: you can build AI agents that interact with tools, APIs, and external services autonomously. The model does not just generate text — it can take actions.

Code Generation

High-quality code generation that works completely offline. Turn your workstation into a local-first AI coding assistant without sending your code to any cloud service.

Vision, Video, and Audio

All models process images and video natively with variable resolutions. They excel at visual tasks like OCR (reading text from images) and chart understanding. The E2B and E4B edge models add native audio input — meaning speech recognition and audio understanding without an internet connection.

140+ Languages

Natively trained on over 140 languages, making it one of the most multilingual open models available. Developers can build applications that serve global audiences without language-specific fine-tuning.

Long Context (Up to 256K Tokens)

The edge models handle 128K tokens of context. The larger models handle 256K — enough to process entire codebases, long documents, or book-length content in a single prompt. For reference, 256K tokens is roughly 500 pages of text.

Why the Apache 2.0 License Matters

Previous Gemma versions used a custom Google license with some restrictions. Gemma 4 switches to Apache 2.0 — the gold standard for open-source software. This is a big deal:

Commercial use: Build and sell products using Gemma 4 with no licensing fees
Modification: Fine-tune, distill, or modify the models however you want
No restrictions: No usage caps, no attribution requirements beyond the license, no geographic limitations
Data sovereignty: Run everything on your own infrastructure — your data never leaves your control

As Hugging Face noted: “The release of Gemma 4 under an Apache 2.0 license is a huge milestone.” For businesses, researchers, and governments concerned about data privacy, this changes the equation significantly.

How to Try Gemma 4 Right Now

You have several options depending on your technical comfort level:

Easiest: Google AI Studio (No Setup)

Go to Google AI Studio and select Gemma 4 31B or 26B MoE. Chat with it in your browser — no installation, no account required beyond a Google login. This is the fastest way to test the model’s capabilities.

Local: Ollama (Run on Your Computer)

If you want to run Gemma 4 on your own machine with no cloud connection:

Install Ollama (free, available for Mac, Windows, Linux)
Open your terminal and run: ollama run gemma4
Start chatting — everything runs locally on your hardware

The 26B MoE model is the best choice for local use — it runs fast on consumer GPUs because it only activates 3.8B parameters per query.

Developer: Hugging Face, vLLM, or LM Studio

For developers who want more control: download the model weights directly from Hugging Face and use your preferred inference framework. Gemma 4 has day-one support for Transformers, vLLM, llama.cpp, MLX, LM Studio, SGLang, NVIDIA NIM, and many more.

Mobile: AI Edge Gallery

For mobile developers: the E2B and E4B models are available through Google’s AI Edge Gallery. Android developers can prototype agentic flows using the AICore Developer Preview for forward-compatibility with Gemini Nano 4.

Gemma 4 vs Other Open Models

How does Gemma 4 compare to other open-source options?

vs Meta’s Llama: Gemma 4 31B outperforms Llama models on the Arena AI leaderboard despite being significantly smaller. The Apache 2.0 license is also more permissive than Meta’s custom license.
vs Mistral: Gemma 4’s MoE architecture offers similar speed advantages to Mistral’s Mixtral, but with better benchmark scores and multimodal capabilities.
vs Qwen (Alibaba): Competitive on benchmarks. Gemma 4’s main advantages are the Apache 2.0 license and Google’s ecosystem support.

For a broader comparison of AI platforms, see our guide on ChatGPT vs Claude vs Gemini.

What This Means for Non-Technical Users

If you are not a developer, Gemma 4 still matters — here is why:

Apps will get smarter. Thousands of apps are built on open models like Gemma. When the base model improves this dramatically, every app built on it gets better.
AI on your phone is about to leap forward. The E2B and E4B models will power next-generation on-device AI features in Android phones — faster, smarter, and working offline.
Privacy improves. More capable local models mean more AI tasks can happen on your device without sending data to the cloud.
Competition benefits everyone. Google releasing frontier-capable models for free puts pressure on every other AI company to improve their offerings.

Frequently Asked Questions

Is Gemma 4 free?

Yes. Gemma 4 is completely free to download, use, modify, and deploy commercially under the Apache 2.0 license. No API fees, no subscription, no usage limits.

What is the difference between Gemma and Gemini?

Gemini is Google’s consumer-facing AI product — you use it through the Gemini app or website. Gemma is the open-source model family that developers download and run on their own hardware. They share research DNA but serve different audiences. Gemma 4 is built from the same technology as Gemini 3.

Can I run Gemma 4 on my laptop?

Yes, with the right model size. The E2B and E4B models run on most modern laptops. The 26B MoE model runs well on laptops with a decent GPU (8GB+ VRAM). The 31B Dense model needs more powerful hardware — a gaming GPU with 16GB+ VRAM, or use a quantized version for smaller GPUs.

What is a Mixture of Experts (MoE) model?

A MoE model has many “expert” sub-networks but only activates a few of them for any given query. The 26B MoE model has 26 billion total parameters but only uses 3.8 billion at a time. You get the knowledge of a large model with the speed of a small one.

What changed with the license?

Previous Gemma versions used a custom Google license with some restrictions. Gemma 4 uses Apache 2.0 — a standard open-source license with virtually no restrictions. This is a significant change for businesses and researchers who were cautious about Google’s custom terms.

Should I use Gemma 4 or ChatGPT?

Different tools for different needs. ChatGPT is easier to use — just open the website and start chatting. Gemma 4 requires some technical setup but gives you complete control, privacy, and no ongoing costs. If you want a ready-made experience, use ChatGPT or Gemini. If you want to build something custom or keep everything private, use Gemma 4.

Sources

Last reviewed: April 4, 2026

Go deeper with AI models. Learn the difference between open-source and closed-source AI, explore Hugging Face where these models live, or see how to run open-source AI on your own computer.

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