Gemma 4 26B A4B is the Mixture-of-Experts model in Google DeepMind's Gemma 4 open-weight family, announced April 2, 2026, with weights on Hugging Face since March 11, 2026. It holds 25.2B total parameters but activates only 3.8B per token, pairing big-model knowledge with small-model speed under an Apache 2.0 license.

Model Specifications

Key Features

• MoE speed: Only ~4B parameters active per token, far faster than a dense model of similar total size.
• 256K context: Long documents and large codebases fit in one prompt.
• Thinking mode: Native reasoning for hard problems, fast responses otherwise.
• Multimodal input: A ~550M-parameter vision encoder reads images alongside text.
• Open and deployable: Apache 2.0 for free commercial use; BF16 fits one 80GB H100, quantized builds run on consumer GPUs.

Use Cases

• Local coding assistants: Interactive IDE help on consumer GPUs, no API needed.
• Agentic workflows: Long context plus thinking mode; Google wires it into Agent Mode in Android Studio.
• Document and image understanding: Long reports, screenshots, and mixed-media analysis.
• Multilingual products: 140+ languages for global apps.

Pricing and Access

The weights are a free Apache 2.0 download, and Google AI Studio hosts the 26B MoE for free trial. There is no per-token API fee beyond hosting: self-host on your own GPU, or pay a third-party provider only for compute.

Advantages vs Competitors

1. Vs Llama: Llama is dense at comparable sizes; Gemma 4 reaches similar quality with ~4B active parameters and ranks #6 among open models on the Arena AI text leaderboard.
2. Vs Qwen: Qwen has strong open MoE flagships, but Gemma 4 adds native Google tooling (AI Studio, AICore on edge, Android Studio Agent Mode) and a 262K vocabulary across 140+ languages.
3. Ecosystem: 400M+ cumulative Gemma downloads and 100K+ community variants bring fast fine-tunes and tooling.

Getting Started

1. Try free: Open Google AI Studio and select the 26B MoE model.
2. Download: Pull gemma-4-26b-a4b-it from Hugging Face (~10.1M downloads, 1,391 likes).
3. Deploy: BF16 on one 80GB H100, or a quantized build on a consumer GPU.

FAQ

Is it free for commercial use?

Yes, Apache 2.0 is commercially permissive.

What hardware do I need?

BF16 fits one 80GB H100; quantized versions run natively on consumer GPUs since only ~3.8B parameters activate per token.

How does it rank?

6 open model on the Arena AI text leaderboard; the 31B sibling ranks #3.

Alternatives

• Meta Llama 3.2 1B Instruct: A much smaller dense Llama for on-device tasks.
• Qwen 3.8 27B: A dense open-weight competitor in a similar class.
• Qwen 3.8 2.4T A95B: A far larger open MoE flagship for maximum capability.

Tips

1. Toggle thinking mode only for hard reasoning.
2. Start with a quantized build locally.
3. Feed whole repos or documents instead of chunking; 256K can take it.