Why Google’s Gemma 3 Is the Best Open-Source AI Model in 2025

Philip Moses
May 6
2 min read

Updated: May 22

The AI landscape is evolving rapidly, and Google continues to be at the forefront with its open-source models. Following the success of Gemma 1 and 2, Google has now unveiled Gemma 3—a powerful, efficient, and highly accessible AI model designed for developers, researchers, and businesses.

Released on March 12, 2025, Gemma 3 builds upon the same technology that powers Google’s Gemini 2.0, offering state-of-the-art performance while being lightweight enough to run on devices from smartphones to workstations.

In this blog, we’ll explore Gemma 3’s key features, performance benchmarks, and how it compares to other leading AI models.

What is Gemma 3?

Gemma 3 is a family of open-weight AI models developed by Google, available in four sizes:

1B (1 billion parameters) – Optimized for mobile and edge devices
4B, 12B, and 27B – Designed for high-performance tasks on GPUs/TPUs

Unlike closed models, Gemma 3 is freely available for modification and deployment, making it ideal for developers who need flexibility.

Key Highlights:

✅ Built on Gemini 2.0’s research & tech

✅ Pre-trained & instruction-tuned variants available

✅ Runs efficiently on a single GPU/TPU

✅ Supports 140+ languages

Key Features of Gemma 3

1. Multimodal Capabilities (Text + Vision)

For the first time in the Gemma series, the 4B, 12B, and 27B models support image and text inputs, enabling:

Image captioning
Visual question answering (VQA)
Document analysis (PDFs, charts, etc.)

Powered by a SigLIP vision encoder, Gemma 3 processes images at 896x896 resolution, making it useful for medical imaging, OCR, and more.

2. Massive 128K Context Window

Gemma 3’s 4B, 12B, and 27B models support 128,000 tokens, meaning they can:

Analyze entire books in one go
Process long legal documents without losing context
Maintain coherent conversations over extended interactions

The 1B model still offers a 32K token window, ideal for lightweight applications.

3. Multilingual & Coding Excellence

Supports 140+ languages, with optimized performance in 35+
Outperforms Llama 3, DeepSeek-V3, and Mistral in multilingual benchmarks
Strong coding abilities (tested on HumanEval, LiveCodeBench)

4. Function Calling & Structured Outputs

Gemma 3 can:

Call APIs dynamically (e.g., fetch weather data, execute code)
Generate JSON-structured responses for easy integration
Power AI agents for automation

5. Quantized Models for Efficiency

Google released 8-bit and 4-bit quantized versions, drastically reducing VRAM usage:

Gemma 3 27B (int4) runs on just 14.1GB VRAM (vs. 54GB for full precision)
Enables local deployment on consumer GPUs (e.g., RTX 4090)

Performance Benchmarks

Gemma 3 outperforms larger models in its class:

Model	MMLU (5-shot)	GSM8K (Math)	HumanEval (Code)
Gemma 3 4B	59.6	38.4	36.0
Gemma 2 27B	55.1	32.7	29.5
Llama 3 8B	54.8	35.2	30.1