Gemma 4 Model Sizes and Architecture
The Gemma 4 family includes four models:
E2B — 2 billion parameters, 128K context. Accepts text, images, video, and audio. You can run this model on absolutely anything: phones, Raspberry Pi, and Jetson Orin Nano. This is the smallest model in the lineup.
E4B — 4 billion parameters, 128K context. Same modality support as E2B, more capacity. Outperforms Gemma 3 12B on most benchmarks despite having three times fewer parameters.
26B MoE (A4B) — 26 billion total parameters with only 4 billion active at any time, thanks to mixture-of-experts routing. 256K context window. Currently ranked #6 on the Arena AI leaderboard. Understands text, images, and video.
31B Dense — 31 billion parameters, all active. 256K context. It’s tanked #3 on Arena AI. The performance is comparable to Claude Opus 4.6, for example.
The split is intentional. E2B and E4B are edge models — offline, on-device, near-zero latency. The 26B and 31B are server-grade models for production workloads and RAG pipelines that need long context.
Gemma 4 Features
Gemma 4 adds five capabilities that Gemma 3 either lacked or only partially supported—and together, they make it feel like an offline ChatGPT.
Built-In Reasoning Mode
All Gemma 4 models support a thinking mode where the model generates step-by-step reasoning before producing a final answer. This improves performance across most tasks.
Multimodal Input
Every model in the family accepts text, images, and video. The E2B and E4B edge models also accept audio input.
140+ Languages
The model offers native multilingual support for over 140 languages, making it highly capable for translation tasks and ideal for users who prefer languages other than English.
Agentic Capabilities
Gemma 4 supports native function calling and structured JSON output. What this means for you as a user is that the models can execute multi-step plans — receive a goal, break it into tasks, call tools, and return results — sort of like OpenClaw.
Edge-First Design
The E2B and E4B models were designed from the start for offline inference — in other words, they’re optimized for offline AI use cases.
Gemma 4 Benchmarks and Performance
These numbers tell the whole story.
| Model |
Type |
Key Metrics |
| 31B Dense |
Dense |
MMLU Pro: 85.2% | AIME 2026: 89.2% | Codeforces ELO: 2150 |
| 26B MoE |
MoE |
4B active parameters per forward pass |
| E4B (4B) |
Edge model |
Outperforms Gemma 3 (12B) on most benchmarks |
| Gemma 3 (12B) |
Dense |
Inferior to E4B on most benchmarks |
Gemma 4 vs Llama 4 vs Qwen 3.5 vs Phi-4
Here's how Gemma 4 stacks up against the main alternatives.
Gemma 4 31B vs Llama 4 Maverick
These models use different architectures: dense vs mixture-of-experts. Llama 4 Maverick slightly outperforms Gemma 4 on benchmarks like MMLU Pro (85.5% vs 85.2%), but it is a 400B-parameter MoE model, which makes inference significantly more expensive and deployment more complex. In contrast, Gemma 4’s dense architecture is much cheaper to run in terms of how much it will tax your hardware.
Licensing is another factor: Llama 4’s community license comes with MAU restrictions and acceptable-use limitations, whereas Gemma 4 is fully Apache 2.0. In practice, Gemma 4 is far more efficient .
Gemma 4 vs Qwen 3.5 27B
This is a very interesting comparison as Qwen 3.5 is another new model family as of the time of writing.
Notably, Gemma 4 has multimodal capabilities — you can chat with video, audio, and images — that Qwen lacks.
Both are Apache 2.0. Qwen will likely perform better in Chinese — a very specific use-case for most people. If you want to use attachments in chats, Gemma 4 is a clear winner. In terms of response quality, you’ll likely feel no difference in everyday chats.
Gemma 4 vs Phi-4 14B
Phi-4 14B is a remarkably efficient model for its size — it achieves a 84.8% on MMLU Pro with only 14B parameters. However, this model is all about optimization and running on low-tier hardware. Primarily, it’s for mobile developers, not people who want to maximize the capital of their local offline AI.
For example, Phi-4 is strictly text-only — no images, video, or audio — so it cannot compete in any multimodal scenario.
In most cases Gemma 4 is a much better choice, unless you’re using very outdated hardware that can’t handle it.
Edge tier
This is a specific use case and it’s going to be irrelevant for most people, but it’s still worth pointing out: none of the other major open models offer equivalents to the E2B or E4B variants, which allow multimodal workloads on embedded devices.
Who Should Use Gemma 4
Five use cases where Gemma 4 is the strongest choice right now:
Offline AI. The E2B and E4B models run on any device — even older laptops and still deliver multimodal capabilities.
Agentic workflows. The native function calling and structured output support make Gemma 4 practical for building agents that plan, use tools, and act autonomously — locally or in the cloud.
Local AI deployments. Apache 2.0 means no vendor lock-in, no usage restrictions, and no licensing surprises. Governments and enterprises that need full control over their AI stack can deploy Gemma 4 anywhere.
Long-context RAG. The 26B and 31B models support 256K tokens of context. That's enough to ingest entire codebases, large legal documents, or knowledge bases.
How to Get Started with Gemma 4
All Gemma 4 models are available now. You can download weights from Kaggle and Hugging Face, or deploy managed instances through Google Cloud's Vertex AI.
You’ll also need a chat interface to communicate with the model. Atomic Chat is the fastest way to get Gemma 4 running on your machine. It's a desktop app built for running open models locally — download the model and start chatting immediately.
Bottom Line
Gemma 4 brings a combination of features no other open model family offers simultaneously:
- Ability to run on ultra-low tier devices
- Ability to process audio and video
- Built-in chain-of-thought support
- Apache 2.0 license
- Frontier-level performance
On top of that, the benchmarks show it competing with models that need ten times the computing power.
If you're looking to run an offline AI chatbot — Gemma 4 is the model to download first, and Atomic Chat is the easiest place to get it set up and running.
