Remote OpenClaw Blog
Gemma 4
4 min read ·
Gemma 4 is Google's April 2026 family of open-weight multimodal models. The official launch material and model cards describe four sizes: E2B, E4B, 26B A4B, and 31B, with Apache 2.0 licensing and context windows from 128K to 256K.
What It Is
Gemma 4 is Google DeepMind's newest open-weight Gemma family, released in April 2026 through the official Gemma 4 launch write-up and Google-published model cards.
The family mixes dense and mixture-of-experts designs, handles text and image input across the lineup, and adds native audio on the small E2B and E4B variants. The same model cards also call out native function calling, long-context support, multilingual coverage, and coding-focused improvements.
Model Lineup
The public Gemma 4 lineup is split between two smaller edge-friendly models and two larger workstation-class models.
| Model | Shape | Context | Notable trait |
|---|---|---|---|
| E2B | 2.3B effective, 5.1B with embeddings | 128K | Smallest option, native audio support |
| E4B | 4.5B effective, 8B with embeddings | 128K | Small local model with audio support |
| 26B A4B | 26B total, 4B active mixture-of-experts | 256K | Large-context MoE option for local servers |
| 31B | 30.7B dense | 256K | Largest dense model in the family |
Those specs come directly from the official E4B card and the 31B card. Google also states that Gemma 4 is pretrained on 140+ languages and offers 35+ languages out of the box for use.
Why It Matters for Agents
Gemma 4 matters because it gives local agent stacks a current open-weight model family with long context and native tool-oriented capabilities.
The official cards call out function calling, coding, image understanding, and long-context reasoning as core capabilities. That combination is useful for agent runtimes that need to read files, use tools, and keep more of a workflow in context without defaulting to a paid API call for every request.
Model Evaluation to Workflow
Build time: 1 hr. Atlas 2: 15 minutes. Your call.
The smaller models also matter for edge deployment. Google's launch material positions E2B and E4B as the on-device and laptop-friendly side of the lineup, while the 26B A4B and 31B models target more serious local hardware.
Which Model to Pick
The right Gemma 4 choice mostly comes down to whether you are optimizing for cheap local inference or stronger reasoning.
Pick E2B or E4B when you care about smaller hardware, local demos, or lightweight assistants. Pick 26B A4B when you want the family member that most plausibly fits serious local agent work without jumping straight to the largest dense model. Pick 31B when you have the memory budget and want the largest dense checkpoint Google has published in this release.
If your actual goal is OpenClaw with Ollama, the better next step is usually a deployment-specific guide rather than the model-family overview. That is why the practical follow-up is our Gemma 4 with Ollama setup guide.
Where It Fits
Gemma 4 fits best as a local-model option for teams that want an open-weight family with current multimodal and long-context support.
It is a stronger fit than older small open models when you specifically need images, longer context, or a clearer path between laptop-scale and workstation-scale deployment inside one family. It is a weaker fit when you only care about the absolute easiest local deployment on minimal hardware or when you are already committed to a different provider stack through Ollama or OpenRouter.
Limitations and Tradeoffs
Gemma 4 is not one model, so broad claims about "Gemma 4 performance" are often too vague to be useful. The smaller models and larger models have different context limits, different modality support, and different hardware expectations. The family is also only part of the local-agent story: runtime support, quantization, VRAM, and the surrounding tool stack matter just as much. If you need the simplest turnkey local setup, the family overview is not enough by itself.
Related Guides
- How to Run Gemma 4 with OpenClaw on Ollama
- Best Ollama Models for OpenClaw
- Best Ollama Models 2026
- Ollama vs OpenRouter for OpenClaw
FAQ
Is Gemma 4 open source?
Gemma 4 is open-weight and Google publishes the model cards under Apache 2.0. That gives you commercial-use and redistribution rights, but you should still read the official model card terms for the exact license context.
What context window does Gemma 4 support?
The official cards say E2B and E4B use 128K context, while 26B A4B and 31B use 256K context.
Does Gemma 4 support audio?
Yes, but the official launch material limits native audio support to the small E2B and E4B models.
Which Gemma 4 model is the practical local choice?
The 26B A4B model is the practical middle ground if you want a larger-context local model without jumping straight to the biggest dense checkpoint.