Google DeepMind releases Gemma 4 family with 256K context window and multimodal capabilities

Google released Gemma 4, a new line of open-weights models available in sizes from 2 billion to 31 billion parameters, under a permissive Apache 2.0 license. The release includes multimodal capabilities, support for 140+ languages, native function calling, and a 256,000-token context window for the larger variants.

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.

Microsoft released public preview versions of three proprietary AI models: MAI-Transcribe-1 for speech recognition across 25 languages at 50% lower GPU cost than alternatives, MAI-Voice-1 for speech synthesis generating 60 seconds of audio in under a second, and MAI-Image-2 for text-to-image generation. The models are available exclusively through Microsoft Azure AI Foundry and already power Copilot, Bing, and PowerPoint.

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 DeepMind released the Gemma 4 family of open-weights multimodal models in four sizes: E2B (2.3B effective), E4B (4.5B effective), 26B A4B (25.2B total, 3.8B active), and 31B dense. All models support text and image input with 128K-256K context windows, reasoning modes, and native function calling for agentic workflows.

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 DeepMind has released Gemma 4 31B, a 30.7-billion-parameter multimodal model supporting text and image input. The model features a 262,144-token context window, configurable thinking/reasoning mode, native function calling, and multilingual support across 140+ languages under Apache 2.0 license.

Augment Code now offers Gemini 3.1 Pro alongside Claude Opus 4.6 and GPT-5.4. In head-to-head testing on structural refactoring tasks, Gemini matched or outperformed Opus while consuming 268 credits per task—46% cheaper than Opus's 488 credits—making it 2.6x more cost-effective per message in real-world usage.

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.

Microsoft AI released three foundational models on April 2: MAI-Transcribe-1 for speech-to-text across 25 languages, MAI-Voice-1 for audio generation, and MAI-Image-2 for video generation. The company positions these models as cheaper alternatives to Google and OpenAI offerings. Models are available on Microsoft Foundry with pricing starting at $0.36 per hour for transcription.

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.