Google's most capable open language model yet. Built for developers, researchers, and enterprises. Reasoning, coding, multilingual - all in one model.
Experience the reasoning, coding, and creative capabilities of Gemma 4 firsthand.
Available through multiple platforms. Choose the method that works best for your workflow.
Download the model weights directly from Hugging Face Hub. Supports GGUF, Safetensors, and PyTorch formats.
pip install transformers
Download on Hugging Face →
Containerized deployment for production environments. Ensures consistency across development, staging, and production setups.
ai.google.dev/gemma
Docker →
User-friendly desktop interface for running Gemma 4 locally. No coding required ideal for beginners and non technical users.
gcloud ai models upload
LM Studio →
Run Gemma 4 locally with Ollama. Optimized for Mac, Linux, and Windows with quantized models.
ollama run gemma4:27b
Run with Ollama →
Access the source code, fine-tuning scripts, and documentation. Contribute to the open ecosystem.
git clone gemma4-repo
View on GitHub →
Experiment with Gemma 4 using free GPU notebooks on Kaggle. Great for learning and prototyping.
kaggle models load gemma4
Open on Kaggle →
Take Gemma 4 from development to production with our flexible deployment options. Choose the platform that matches your infrastructure needs and scale requirements:
High-performance numerical computing library optimized for machine learning research. Ideal for custom training loops, distributed computing, and cutting-edge experimentation with maximum flexibility.
Install JAX for CPU, GPU or TPU.
Installation on Jax →
Google Cloud's unified ML platform for building, training, and deploying models at scale. Features automated ML, MLOps tools, and seamless integration with Google Cloud services.
ai.google.dev/gemma
Vertex AI →
User-friendly deep learning framework with intuitive APIs. Perfect for rapid prototyping, educational purposes, and standard neural network architectures with minimal code.
gcloud ai models upload
Keras →
Deploy Gemma 4 on mobile and edge devices with TensorFlow Lite. Optimize for on-device inference with reduced latency and enhanced privacy for iOS and Android applications.
Deploy AI across mobile, web, and embedded applications
Google AI Edge →
Enterprise-grade container orchestration for scalable deployments. Auto-scaling, load balancing, and high availability for production workloads serving millions of users.
Run Gemma with Kubernetes Engine
Google Kubernetes Engine (GKE) →
Lightweight local deployment for development and testing. Run Gemma 4 on your machine with minimal setup, perfect for prototyping before scaling to cloud infrastructure.
Run Gemma with Ollama
Ollama →
How does Gemma 4 stack up against leading language models? Here's a detailed comparison.
| Model | MMLU | HumanEval | GSM8K | Context | Open Source | Score |
|---|---|---|---|---|---|---|
Gemma 4 (27B) |
92.4 | 94.1 | 96.2 | 1M | ✅ Yes | |
GPT-4o (ChatGPT) |
88.7 | 90.2 | 95.0 | 128K | ❌ No | |
Claude 3.5 Sonnet |
90.1 | 92.0 | 94.8 | 200K | ❌ No | |
Qwen 2.5 Max |
89.5 | 88.4 | 93.1 | 256K | ⚠️ Partial | |
Kimi k2 |
86.2 | 82.3 | 90.5 | 2M | ❌ No | |
Llama 3.3 70B |
86.0 | 84.1 | 91.2 | 128K | ✅ Yes | |
Mistral Large 2 |
84.0 | 80.5 | 88.7 | 128K | ❌ No | |
DeepSeek V3 |
87.8 | 86.0 | 92.4 | 128K | ✅ Yes |
* Scores are approximate and based on publicly available benchmark data as of 2026.
Built from the ground up with cutting-edge research and real-world developer feedback.
Multi-step logical reasoning with chain-of-thought capabilities. Solves complex math, science, and logic problems with state-of-the-art accuracy.
Write, debug, and refactor code across 50+ programming languages. Supports full project-level understanding and agentic coding workflows.
Truly multilingual with native-quality understanding and generation in over 100 languages including low-resource languages.
Process entire books, codebases, or long documents in a single prompt with exceptional recall and attention across the full context window.
Advanced safety filters, responsible AI guardrails, and fine-grained content moderation built directly into the model architecture.
Fast inference with TPUs, GPUs, and even edge devices. Quantized models available for deployment on consumer hardware.
Full support for LoRA, QLoRA, and full fine-tuning. Pre-built scripts and integrations with popular ML frameworks.
Function calling, tool use, and autonomous agent workflows. Build AI agents that can interact with APIs, databases, and external systems.
Supports text, images, and structured data inputs. Analyze charts, diagrams, and visual content alongside textual reasoning.
Everything you need to know about Gemma 4.
Gemma 4 is Google's latest generation of open language models, built on the same research and technology that powers Gemini. It features 27 billion parameters, a 1M token context window, and state-of-the-art performance on reasoning, coding, and multilingual benchmarks.
Yes! Gemma 4 is open and free for research, personal, and commercial use under Google's Gemma license. You can download the weights from Hugging Face, run it locally with Ollama, or access it via the free tier of Google AI Studio.
Gemma 4 outperforms GPT-4o and Claude 3.5 Sonnet on several key benchmarks including MMLU (92.4 vs 88.7/90.1), HumanEval coding (94.1 vs 90.2/92.0), and GSM8K math reasoning (96.2 vs 95.0/94.8). Unlike those closed models, Gemma 4 is fully open source.
For the full 27B model, we recommend a GPU with at least 48GB VRAM (e.g., A6000). Quantized versions (4-bit/8-bit) can run on consumer GPUs with 16-24GB VRAM. The 2B variant runs comfortably on most modern laptops and even mobile devices.
Absolutely. Gemma 4 supports LoRA, QLoRA, and full fine-tuning. We provide pre-built training scripts compatible with Hugging Face Transformers, JAX, and PyTorch. Fine-tuning guides and example notebooks are available on our GitHub repository.
Gemma 4 is the open-weight version derived from Gemini research. While Gemini is Google's proprietary model available through Google services, Gemma 4 is released with open weights so anyone can download, modify, and deploy it on their own infrastructure.
Yes! Gemma 4 is available through the Google AI Studio API and Vertex AI API. You can get a free API key with generous rate limits. We also support OpenAI-compatible endpoints so you can easily swap providers in existing applications.
Gemma 4 was developed by Google DeepMind as part of the Gemma family of open AI models.
Gemma 4 is generally described as an open-weight model family. That means developers can access and use the model weights under Google’s licensing terms, but it is not the same as unrestricted open-source software in every sense.
Gemma 4 can handle reasoning, text generation, image understanding, coding help, long-context analysis, multilingual tasks, and tool-based AI workflows.
Yes. Gemma 4 supports multimodal input, including text and image input, and some variants also support audio-related input capabilities.
Gemma 4 supports long context windows, with some versions offering up to 256K tokens, making it suitable for large documents, long conversations, and codebase analysis.
Gemma 4 supports more than 140 languages, making it useful for multilingual apps, assistants, and international AI products.
Common Gemma 4 use cases include AI agents, coding assistants, research tools, document summarization, multilingual chatbots, enterprise knowledge systems, and visual understanding applications.
Yes. Gemma 4 is well suited for coding support, especially in workflows that require long context, reasoning, code explanation, structured output, and tool use.
Yes. Gemma 4 is designed with agentic workflows in mind, which makes it a strong candidate for AI agents that plan tasks, call tools, and operate across multi-step workflows.
Gemma 4 improves on Gemma 3 with a stronger focus on reasoning, larger context support in some variants, and broader support for advanced developer workflows.
Yes. Gemma 4 supports image input, which helps developers build visual AI applications such as document analysis tools and image-aware assistants.
Gemma 4 is designed to be efficient and flexible, so some variants are suitable for local or edge deployment depending on available hardware.
Gemma 4 is intended for developer and business use, but commercial use depends on the exact license terms and compliance with Google’s model usage conditions.
Yes. Gemma 4 is attractive for startups and developers who want strong AI capabilities with more control over deployment, tuning, and infrastructure choices.
Gemma 4 can be used through supported developer platforms and tooling, and developers can also integrate it into their own workflows depending on deployment method.
Gemma 4 can be used in software development, education, research, customer support, enterprise productivity, content creation, healthcare documentation support, and multilingual services.
Like other AI models, Gemma 4 can still make mistakes, require prompt tuning, need safety checks, and depend heavily on deployment quality and evaluation methods.
That depends on the use case. Gemma 4 offers more flexibility and deployment control, while some closed models may still lead in raw performance, ecosystem convenience, or specialized features.
Gemma 4 is a strong option if you want an open-weight model for reasoning, multimodal AI, long context, and developer-focused deployment flexibility.
Start building AI-powered applications in minutes. Get your free API key and integrate Gemma 4 into your products today.
// Install the SDK pip install google-genai // Initialize the client from google.genai import Client client = Client(api_key="YOUR_API_KEY") // Generate a response response = client.models.generate_content( model="gemma-4-27b", contents="Explain quantum computing" ) print(response.text)