Gemma 4 26B-A4B

The Gemma 4 to start with: a mixture-of-experts model that gives 26B-class quality at roughly 4B-class cost, multimodalA model that can handle more than one type of input or output — typically text plus images, sometimes plus audio or video. "GPT-4 Vision" and "Llama 3.2 11B Vision" are multimodal models that accept both text and images. A text-only model is called "unimodal" but nobody uses that term; text-only is the assumed default. and Apache 2.0, comfortable on a single consumer GPUA GPU designed for desktop PCs and gaming — typically Nvidia RTX 3090, 4090, 5090 or similar. Consumer GPUs have 8-32GB of VRAM and cost a few thousand dollars each. Capable of running small and medium models, especially when quantized. The boundary between "runs on a consumer GPU" and "needs a datacenter GPU" roughly separates small from large models in the catalog..

Gemma 4 26B-A4B is the size Google itself points most people to, and the logic is easy to see. It's a mixture-of-experts model: 26 billion parameters total, but only about 4 billion active for any given tokenThe basic unit of text a model reads and writes. Tokens are roughly three-quarters of a word in English — so 100 tokens is about 75 words. Models don't see letters or words directly; they see tokens. Pricing is almost always quoted per million tokens, and context windows are measured in tokens rather than words.. That means it carries the knowledge and quality of a 26B model while running with the speed and memory footprint closer to a 4-billion-parameter one — fast, cheap, and easy to fit on hardware most teams already have.

Like the rest of Gemma 4 it's natively multimodalA model that can handle more than one type of input or output — typically text plus images, sometimes plus audio or video. "GPT-4 Vision" and "Llama 3.2 11B Vision" are multimodal models that accept both text and images. A text-only model is called "unimodal" but nobody uses that term; text-only is the assumed default. (text, images, and audio in), covers a wide span of languages, and ships under clean Apache 2.0. The practical effect is a model you can self-host on a single consumer GPUA GPU designed for desktop PCs and gaming — typically Nvidia RTX 3090, 4090, 5090 or similar. Consumer GPUs have 8-32GB of VRAM and cost a few thousand dollars each. Capable of running small and medium models, especially when quantized. The boundary between "runs on a consumer GPU" and "needs a datacenter GPU" roughly separates small from large models in the catalog., get genuinely strong results from, and deploy commercially with no licensing friction. For a business standing up its first in-house AI capability, this is a very sensible default — capable enough for real work, light enough to run affordably, and open enough to own.

If you later find you need more raw quality, Gemma 4 31B is the step up; if you need to go smaller for the edge, Gemma 4 E2B is the step down. This one sits in the middle on purpose.

• Teams standing up their first self-hostedRunning a model on hardware you control — your own servers, your own cloud instance, or your own laptop — rather than paying to access it through someone else's API. Self-hosting gives you full control over data and predictable costs, but requires the hardware and operational effort to run the model. Only possible with open-weight models. model who want the best quality-per-dollar default.
• Production workloads that need to be fast and cheap to run while staying capable.
• MultimodalA model that can handle more than one type of input or output — typically text plus images, sometimes plus audio or video. "GPT-4 Vision" and "Llama 3.2 11B Vision" are multimodal models that accept both text and images. A text-only model is called "unimodal" but nobody uses that term; text-only is the assumed default. applications (image and audio understanding) on a budget, self-hosted.
• Anyone who wants Gemma 4's capability without the 31B's heavier footprint.

• Squeezing out the absolute best scores — Gemma 4 31B and the closed Gemini 3.5 Flash go higher.
• The smallest edge devices — phones and IoT want Gemma 4 E2B.
• Teams that specifically prefer a dense modelA model where every parameter is used for every input — the entire model runs on every token. Contrast with sparse or Mixture of Experts models, which activate only a fraction of the model per input. Dense models are simpler and more predictable; MoE models are more efficient at scale.'s predictable per-tokenThe basic unit of text a model reads and writes. Tokens are roughly three-quarters of a word in English — so 100 tokens is about 75 words. Models don't see letters or words directly; they see tokens. Pricing is almost always quoted per million tokens, and context windows are measured in tokens rather than words. behavior over MoEA model architecture that splits the model into many smaller specialized "expert" networks, only activating a handful per input rather than running the whole model every time. The practical effect: you get the knowledge capacity of a big model with the compute cost of a much smaller one. Mistral Large 3 and Mistral Small 4 are both MoE models. routing.

Apache 2.0 — unrestricted commercial use, modification, fine-tuning, and redistribution, no royalties or carve-outs; keep the notices and flag significant changes. As with all of Gemma 4, this is a clean break from the older custom Gemma terms, so confirm you're on version 4. Self-hostedRunning a model on hardware you control — your own servers, your own cloud instance, or your own laptop — rather than paying to access it through someone else's API. Self-hosting gives you full control over data and predictable costs, but requires the hardware and operational effort to run the model. Only possible with open-weight models., it keeps all your data in-house.

Against Gemma 4 31B, the 26B-A4B is faster and cheaper to run for a small quality trade — the better everyday choice for most. Against Gemma 4 E2B, it's far more capable but needs a real GPUThe specialized chip that runs most AI models. Originally designed for 3D graphics, GPUs turned out to be excellent at the math AI requires. Nvidia dominates the AI GPU market; common datacenter models include the H100, H200, and B200. Running an AI model without a GPU is possible but painfully slow for anything but the smallest models. rather than a phone. Against open MoEA model architecture that splits the model into many smaller specialized "expert" networks, only activating a handful per input rather than running the whole model every time. The practical effect: you get the knowledge capacity of a big model with the compute cost of a much smaller one. Mistral Large 3 and Mistral Small 4 are both MoE models. models from Qwen (like the 30B-A3B) and others, Gemma 4 26B-A4B competes on efficiency and multimodality under a similarly clean Apache 2.0 license, with US jurisdiction as a differentiator for data-governance-sensitive buyers.