gemma-4

Google DeepMind released Gemma 4, a family of open-weights multimodal models in four sizes: E2B (2.3B effective), E4B (4.5B effective), 26B A4B (3.8B active), and 31B (30.7B parameters). All models support text and image input with 128K-256K context windows, while E2B and E4B add native audio capabilities and reasoning modes across 140+ languages.

Google DeepMind released the Gemma 4 family of open-source models with multimodal capabilities (text, image, audio, video) and context windows up to 256K tokens. Four distinct model sizes—E2B (2.3B effective parameters), E4B (4.5B effective), 26B A4B (3.8B active), and 31B—are available under the Apache 2.0 license, with instruction-tuned and pre-trained variants.

Google has released Gemma 4, a family of four open models ranging from 2B to 31B parameters, now available under the Apache 2.0 license for the first time. The 31B dense model ranks 3rd on the Arena AI Text Leaderboard, while the 26B mixture-of-experts variant ranks 6th, both outperforming significantly larger competitors. All models support multimodal inputs and are available on Hugging Face, Kaggle, and Ollama.

Google has previewed Gemini Nano 4, a new on-device language model for Android, available now in early access via AICore Developer Preview. The model comes in two versions: Gemini Nano 4 Fast (3x faster than previous models, 60% less battery) and Gemini Nano 4 Full (higher reasoning capability). The models will launch on new flagship Android devices later this year.

Google DeepMind released the Gemma 4 family of open-weights models ranging from 2.3B to 31B parameters, featuring up to 256K token context windows and native support for text, image, video, and audio inputs. The flagship 31B model scores 85.2% on MMLU Pro and 89.2% on AIME 2026, with a smaller 26B MoE variant requiring only 3.8B active parameters for faster inference.

NVIDIA has optimized Google's Gemma 4 models for local deployment on RTX and Spark platforms, targeting the emerging wave of on-device agentic AI. The optimization enables small, efficient models to access real-time local context for autonomous decision-making without cloud dependency.

Google DeepMind released Gemma 4, a family of four open models ranging from 2B to 31B parameters, all licensed under Apache 2.0. The 31B dense model ranks #3 on Arena AI's text leaderboard and the 26B mixture-of-experts variant ranks #6, outperforming closed models significantly larger in size.