Google releases Gemma 4 26B with 256K context and multimodal support, free to use
Google DeepMind has released Gemma 4 26B A4B, a free instruction-tuned Mixture-of-Experts model with 262,144 token context window and multimodal capabilities including text, images, and video input. Despite 25.2B total parameters, only 3.8B activate per token, delivering performance comparable to larger 31B models at reduced compute cost.
Gemma 4 26B A4B IT — Quick Specs
Google Releases Gemma 4 26B with 256K Context and Multimodal Support, Free
Google DeepMind has released Gemma 4 26B A4B, a free Mixture-of-Experts model available immediately. The model features a 262,144 token context window, native support for text, images, and video input (up to 60 seconds at 1fps), and is released under Apache 2.0 license.
Model Architecture and Performance
Gemma 4 26B employs a sparse Mixture-of-Experts architecture with 25.2B total parameters but only 3.8B active parameters per token during inference. Google claims this configuration delivers performance comparable to larger 31B dense models while requiring substantially less compute. The model is instruction-tuned and includes native function calling, structured output support, and configurable thinking/reasoning mode for step-by-step problem solving.
Multimodal Capabilities
Unlike earlier Gemma variants, Gemma 4 26B supports multimodal input across text, images, and video. Video support handles sequences up to 60 seconds sampled at 1 frame per second, enabling analysis of temporal content without requiring separate video understanding components.
Pricing and Availability
The model is available for free with zero cost per million input tokens and zero cost per million output tokens. It is accessible via OpenRouter, which routes requests across multiple providers and manages fallback routing to maximize uptime. Model weights are available for local deployment under Apache 2.0 license.
What This Means
Gemma 4 26B represents a significant shift in Google's open model strategy—pairing genuine multimodal capabilities with a sparse architecture that reduces inference costs. The 256K context window matches or exceeds most competitive models, and free pricing removes adoption barriers. For developers, this addresses a clear gap: capable open models with video understanding have been limited. The sparse MoE design is particularly relevant for cost-sensitive deployments where inference happens at scale. The reasoning mode addition suggests Google is matching OpenAI's o1-style thinking patterns in its open offerings.
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