Gemma 4: Google's Most Capable Open Model for AI Agents

What Is Gemma 4?

Gemma 4 is Google DeepMind's latest family of open-weight language models, released on April 2, 2026. It is the most capable open model family Google has ever published, with four variants ranging from 2 billion to 31 billion parameters — all released under the Apache 2.0 license for unrestricted commercial use.

The release represents a significant shift in what open models can do. Previous open-weight models from Google (Gemma 1, 2, and 3) were competitive with smaller proprietary models but could not match frontier performance. Gemma 4's larger variants now rank among the top models on public benchmarks, beating proprietary models many times their size.

For OpenClaw operators, Gemma 4 matters because it makes fully local, zero-API-cost agents a realistic option for production use — not just a demo or experiment.

Official links:

• Google Blog announcement
• Google DeepMind Gemma 4 page

Model Variants

Gemma 4 ships in four variants, each designed for a different deployment scenario:

The 26B MoE (Mixture of Experts) variant is notable because it only activates about 8 billion parameters per inference pass, giving you performance close to the 31B Dense model with significantly lower memory and compute requirements. This makes it the sweet spot for VPS deployments where RAM is limited.

Key Specifications

All four Gemma 4 variants share a common set of capabilities:

• Context window: Up to 256,000 tokens — enough to process entire codebases, long documents, or extended conversation histories
• Native vision: Can process images directly without a separate vision encoder, enabling screenshot analysis, document parsing, and visual understanding
• Native audio: Can process audio input for transcription, understanding, and spoken-language tasks
• Multilingual: Supports 140+ languages out of the box, making it viable for international deployments
• License: Apache 2.0 — no restrictions on commercial use, modification, or redistribution
• Weights: Fully open — you can download, fine-tune, quantize, and redistribute the model weights

The 256K context window is particularly significant for AI agent use cases. It means Gemma 4 can hold an entire day's worth of conversations, a full project codebase, or dozens of documents in a single context — something that was previously only available through proprietary API models like Claude or GPT-4.

Performance and Benchmarks

Gemma 4's benchmark results are what set it apart from previous open model releases:

• 31B Dense: Ranked 3rd on the Arena AI text leaderboard at launch, beating models with 20x more parameters
• 26B MoE: Ranked 6th on the same leaderboard, achieving near-31B performance with significantly lower compute
• 2B and 4B Effective: Best-in-class for their size category on standard benchmarks (MMLU, HumanEval, GSM8K)

What makes these numbers remarkable is the parameter efficiency. The 31B Dense model competes with models in the 400B-600B range on several benchmarks. The 26B MoE model, which only activates ~8B parameters per forward pass, matches or exceeds many 70B dense models.

For practical use, this means you can run a model on a $10/month VPS (26B MoE with quantization) that delivers response quality comparable to what previously required a $200+/month API subscription to a frontier model.

Edge and Mobile Capabilities

The 2B Effective and 4B Effective variants are purpose-built for running on devices with limited resources:

• Minimal RAM footprint: The 2B model runs in under 4GB of RAM, making it viable on most modern smartphones
• Low battery impact: Optimized inference that minimizes power consumption during sustained use
• On-device privacy: All processing happens locally — no data leaves the device
• Offline capable: Once downloaded, the model runs without any internet connection

For OpenClaw operators, the edge variants open up possibilities for running lightweight agents on devices that cannot connect to cloud APIs — field workers, traveling operators, or privacy-sensitive deployments where no data should leave the local network.

Built From Gemini 3

Gemma 4 is derived from the Gemini 3 architecture — Google's proprietary frontier model. This is significant because it means Gemma 4 inherits architectural innovations from a model that competes directly with Claude and GPT-4o, then makes those innovations available under an open license.

Key architectural features inherited from Gemini 3:

• Efficient attention mechanisms that enable the 256K context window without proportional memory scaling
• Native multimodal processing where vision and audio are handled by the same model rather than separate encoders
• Improved instruction following with better adherence to complex, multi-step prompts
• Reduced hallucination rates compared to Gemma 3 and other open models in the same size range

Google has not published the full architectural details, but the Gemma 4 technical report confirms that the training pipeline, data mixtures, and post-training alignment all derive from the Gemini 3 process.

How to Use Gemma 4 With OpenClaw

Gemma 4 can serve as the inference backend for OpenClaw through Ollama, replacing Claude or GPT-4 API calls with a fully local model. This eliminates API costs entirely.

Step 1: Install Ollama

If you do not already have Ollama installed:

# macOS / Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Verify installation
ollama --version

Step 2: Pull the Gemma 4 Model

Choose the variant that matches your hardware:

# For workstations with 32GB+ RAM — best quality
ollama pull gemma4:31b

# For VPS or machines with 16-24GB RAM — best balance
ollama pull gemma4:26b-moe

# For lightweight setups with 8GB RAM
ollama pull gemma4:4b

# For edge devices or minimal setups
ollama pull gemma4:2b

Step 3: Configure OpenClaw to Use Gemma 4

Update your OpenClaw configuration to point to the local Ollama instance:

# In your OpenClaw config (e.g., ~/.openclaw/config.yaml)
llm:
  provider: ollama
  model: gemma4:31b
  base_url: http://localhost:11434
  temperature: 0.7
  max_tokens: 8192

Step 4: Verify the Connection

# Test that Ollama is serving the model
curl http://localhost:11434/api/generate -d '{
  "model": "gemma4:31b",
  "prompt": "Hello, are you running?",
  "stream": false
}'

# Start OpenClaw
openclaw start

Performance Expectations

Running Gemma 4 locally through Ollama gives you zero API costs, but there are trade-offs to understand:

• Response quality: The 31B Dense model approaches Claude Sonnet quality for most tasks but may fall short on complex reasoning or creative writing
• Speed: Token generation will be slower than API calls, especially on machines without a GPU. Expect 10-30 tokens/second on a modern CPU, 50-100+ with a GPU
• Context usage: The 256K context window is supported, but very long contexts will increase latency significantly on local hardware
• Vision and audio: Multimodal features work through Ollama but require additional configuration

For a full guide to running local models with OpenClaw, see Best Ollama Models for OpenClaw.

Comparison to Other Open Models

Gemma 4 enters a competitive open model landscape. Here is how it compares to the other major options as of April 2026:

Key observations:

• Gemma 4 31B outranks models 2-4x its size — achieving 3rd place on Arena against 70B+ models demonstrates exceptional parameter efficiency
• The 256K context window is the longest among major open models, doubling the 128K offered by Llama 3.3 and Qwen 2.5
• Native audio support is unique — no other major open model offers built-in audio processing
• Apache 2.0 is the most permissive license — Llama and Qwen have custom licenses with some restrictions, while Gemma 4 and Mistral Large 2 use Apache 2.0
• The 26B MoE variant is the efficiency champion — ranking 6th while only using ~8B active parameters per inference makes it the most compute-efficient high-performance open model available

For a full breakdown of which models work best with OpenClaw, see Best Ollama Models 2026.

Licensing and Commercial Use

All four Gemma 4 variants are released under the Apache 2.0 license, which is the most permissive mainstream open-source license available. This means:

• Commercial use: You can use Gemma 4 in commercial products and services with no licensing fees
• Modification: You can fine-tune, quantize, distill, or otherwise modify the model weights
• Redistribution: You can distribute Gemma 4 or models derived from it
• No royalties: Google does not charge any fees for using Gemma 4 in any context
• Patent grant: Apache 2.0 includes an explicit patent grant, protecting users from patent claims

This is a meaningful improvement over previous Google model releases and over competitor licenses. Llama 3.3's community license, for example, includes restrictions for companies with over 700 million monthly active users. Gemma 4's Apache 2.0 license has no such restrictions.

For OpenClaw operators building products or services on top of their agent, this means no licensing complications — you can run Gemma 4 as your inference backend and charge for the services your agent provides without any concerns about model licensing.

Frequently Asked Questions

Can I run Gemma 4 with OpenClaw?

Yes. You can run any Gemma 4 variant locally using Ollama and configure OpenClaw to use it as the inference backend instead of Claude or GPT-4. This gives you a fully local, zero-API-cost AI agent — though response quality will vary compared to frontier models like Claude Sonnet or GPT-4o. See the integration guide above for step-by-step instructions.

Which Gemma 4 variant should I use?

For OpenClaw on a workstation or VPS with 32GB+ RAM, use the 31B Dense model for the best response quality. For a lightweight VPS with 16GB RAM, the 26B MoE model offers strong performance with lower memory requirements. For edge devices or smartphones, the 2B Effective or 4B Effective models are designed for minimal RAM and battery impact.

Is Gemma 4 free for commercial use?

Yes. All Gemma 4 variants are released under the Apache 2.0 license, which permits commercial use, modification, and redistribution with no royalties or restrictions. You can use Gemma 4 in production applications, sell products built on it, and modify the weights without any licensing fees.

Further Reading

• Best Ollama Models for OpenClaw — which local models work best for each use case
• Best Ollama Models 2026 — the complete ranking of open models for local inference
• Google Blog: Gemma 4 announcement
• Google DeepMind: Gemma 4 model page