# Gemma 4 26B A4B ![Organization](https://img.shields.io/badge/Org-Google-4285F4) ![Architecture](https://img.shields.io/badge/Arch-MoE%2026B%2FA4B-1f6feb) ![Context](https://img.shields.io/badge/Context-128K%20tokens-1f6feb) ![License](https://img.shields.io/badge/License-Gemma%20ToU-d29922) ![Weights](https://img.shields.io/badge/Weights-Open-2da44e) ![Released](https://img.shields.io/badge/Released-Oct%202025-555) > **Quick answer:** Gemma 4 26B A4B is Google's October 2025 MoE multimodal model with 26B total and 4B active parameters, scoring 82.6% MMLU-Pro. It is the largest model in the Gemma 4 family, using a Mixture-of-Experts architecture for efficient inference at 4B active parameter cost. ## At a Glance **Where Gemma 4 26B A4B leads** - 82.6% MMLU-Pro — strong academic knowledge with 4B active params - MoE: 26B total, 4B active — inference cost of a 4B model, quality of a 26B - Multimodal: text and image inputs - Open weights under Gemma Terms of Use - Latest Gemma generation (Q4 2025) **Where it lags** - Gemma Terms of Use — commercial restrictions - 4B active params limits maximum throughput quality vs dense models - Limited benchmark coverage publicly available **Best for:** Google ecosystem deployments requiring strong MMLU-Pro at 4B inference cost; multimodal tasks needing efficient open-weight MoE. ## What Gemma 4 26B A4B Is Gemma 4 26B A4B is the largest model in Google's Gemma 4 family — the first Gemma generation to use Mixture-of-Experts (MoE) architecture. The "A4B" suffix indicates 4 billion active parameters per forward pass, despite the 26B total parameter count. This MoE approach delivers higher model capacity at lower inference cost. The 82.6% MMLU-Pro score is notable for a model with 4B active parameters — comparable to models that use 13-27B dense parameters. This demonstrates the efficiency benefits of MoE training: broader knowledge with manageable inference compute. Gemma 4 26B A4B targets teams who need stronger capabilities than Gemma 3 27B (67.5% MMLU-Pro) while maintaining on-device or efficient inference characteristics. The multimodal capability extends to image understanding alongside improved text performance. ## Specifications | Field | Value | |---|---| | Organization | Google | | Total parameters | 26B (MoE) | | Active parameters | 4B per forward pass | | Context window | 128,000 tokens | | License | Gemma Terms of Use | | HuggingFace | google/gemma-4-26b-a4b-it | | Release date | October 2025 | | Knowledge cutoff | June 2025 | | Modality | Text + Vision (multimodal) | | Architecture | Mixture-of-Experts (MoE) | ## Pricing Open weights under Gemma Terms of Use — free to self-host. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [MMLU-Pro](/benchmarks/mmlu-pro) | 82.6% | Benchgen evaluation | 2025-10 | ## Gemma 4 26B A4B vs Alternatives | Model | MMLU-Pro | Active Params | Vision | License | |---|---|---|---|---| | **Gemma 4 26B A4B** | 82.6% | 4B | Yes | Gemma ToU | | [Gemma 3 27B](/models/google/gemma-3-27b) | 67.5% | 27B (dense) | Yes | Gemma ToU | | [Gemma 4 E4B](/models/google/gemma-4-e4b) | 69.4% | ~4B | Yes | Gemma ToU | | [Llama 4 Maverick](/models/meta/llama-4-maverick) | 80.5% | 17B active | Yes | Llama 4 | Gemma 4 26B A4B vs Gemma 3 27B: +15.1pp MMLU-Pro (82.6% vs 67.5%) at same inference cost tier — a significant generation-on-generation improvement. ## Related Models & Benchmarks - Compare: [Gemma 3 27B](/models/google/gemma-3-27b) · [Gemma 4 E4B](/models/google/gemma-4-e4b) · [Llama 4 Maverick](/models/meta/llama-4-maverick) - Benchmarks: [MMLU-Pro](/benchmarks/mmlu-pro) ## Frequently Asked Questions

What is Gemma 4 26B A4B?

Google's October 2025 MoE multimodal model with 26B total and 4B active parameters, scoring 82.6% MMLU-Pro. The latest and most capable Gemma 4 model with MoE architecture.

What does A4B mean in Gemma 4?

A4B = 4B Active parameters. In MoE models, only a subset of total parameters are used per forward pass. Gemma 4 26B A4B uses 4B active parameters (matching inference cost to a 4B dense model) while having 26B total trained weights.

Should I use Gemma 4 26B A4B or Gemma 3 27B?

Gemma 4 26B A4B for new deployments: +15.1pp MMLU-Pro, MoE architecture for efficiency, more recent knowledge cutoff (June 2025 vs September 2024).

--- *Specs from Google's official Gemma 4 release (October 2025) and Benchgen evaluations. Last updated 2026-07-24.*

Gemma 4 26B-A4B — Evaluations

Evaluations