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Google DeepMind released Gemma 4, a family of open-weight multimodal models ranging from 2.3B to 30.7B parameters. The flagship 12B Unified model eliminates separate encoders, processing text, images, audio, and video directly through a single decoder-only transformer with up to 256K token context window.
Google DeepMind has released Gemma 4 12B Unified, an encoder-free multimodal model that processes text, images, and audio through a single decoder-only transformer. The model features 11.95 billion parameters, a 256K token context window, and achieves 77.2% on MMLU Pro and 72.0% on LiveCodeBench v6.
Google DeepMind has integrated its Genie world model with Street View's 280 billion images spanning 110 countries, enabling users to explore AI-generated simulations of real locations. Waymo is already using Genie 3 to train self-driving cars on rare scenarios like tornadoes and unexpected obstacles.
Google DeepMind released the Gemma 4 E4B assistant model using Multi-Token Prediction (MTP) architecture that accelerates inference by up to 2x through speculative decoding. The 4.5B effective parameter model supports 128K context windows and handles text, image, and audio input with pricing not yet disclosed.
Google DeepMind has released a Multi-Token Prediction assistant model for Gemma 4 26B A4B that achieves up to 2x decoding speedup through speculative decoding. The model uses 3.8B active parameters from a 25.2B total parameter MoE architecture with 128 experts and a 256K token context window.
Google DeepMind has released Gemma 4, a family of open-weight multimodal models including a 31B dense model with 256K context window and four size variants ranging from 2.3B to 30.7B effective parameters. The release includes Multi-Token Prediction (MTP) draft models that achieve up to 2x decoding speedup through speculative decoding while maintaining identical output quality.
Google DeepMind launched Gemini 3.1 Flash TTS, a text-to-speech model that achieved an Elo score of 1,211 on the Artificial Analysis TTS leaderboard. The model introduces audio tags that allow developers to control vocal style, pace, and delivery through natural language commands embedded in text input, with support for 70+ languages.
Google DeepMind released Gemma 4, an open-weights multimodal model family in four sizes (2.3B to 31B parameters) with context windows up to 256K tokens. All models support text and image input, with audio native to E2B and E4B variants. The Gemma 4 31B dense model scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench—significant improvements over Gemma 3.