Google AI coding assistant for VS Code and JetBrains.
Gemini Code Assist is Google enterprise AI coding assistant available as a plugin for VS Code and JetBrains IDEs. It offers inline completions, AI chat, code transformation, and test generation powered by Gemini models.
Live preview · cloud.google.com/gemini/docs/codeassist/overview
Google DeepMind researchers developed GenCeption, which repurposes Alibaba's Wan2.1 video generator for computer vision tasks including depth estimation, segmentation, and 3D pose estimation. The model matches state-of-the-art specialized systems while training on only 7,500 synthetic videos—between 7 and 500 times less data than competing approaches.
Google has released Gemini 3.1 Flash Lite Image, also called Nano Banana 2 Lite, which the company describes as its fastest and cheapest image generation model. The model is available through Google's AI Studio and Gemini API with the identifier gemini-3.1-flash-lite-image.
Google DeepMind released Nano Banana 2 Lite (gemini-3.1-flash-lite-image), its fastest image generation model with 4-second text-to-image latency priced at $0.034 per 1K-resolution image. The company also opened developer access to Gemini Omni Flash (gemini-omni-flash-preview) for video generation and editing at $0.10 per second of output.
NVIDIA released a quantized version of Google DeepMind's DiffusionGemma 26B A4B IT, a multimodal model with 25.2B total parameters (3.8B active) that processes text, image, and video inputs. The NVFP4-quantized model achieves generation speeds exceeding 1,100 tokens per second on NVIDIA H100 GPUs while supporting a 256K token context window.
Amazon Web Services today announced availability of Google DeepMind's Gemma 4 family on Amazon Bedrock. The open-weight models include three instruction-tuned variants spanning 2.3B to 30.7B parameters, with 256K context windows, multimodal input support, and built-in reasoning mode.
Google DeepMind released DiffusionGemma, a 26B parameter mixture-of-experts model that generates text using discrete diffusion instead of autoregression. The model processes blocks of 256 tokens in parallel, achieving generation speeds exceeding 1100 tokens per second on H100 GPUs in low-batch settings.
Google DeepMind has released Gemma 4 12B, a 12-billion parameter multimodal model that runs locally on laptops with 16GB of RAM. The model eliminates separate vision and audio encoders, processing raw inputs directly through its language model backbone under an Apache 2.0 license.
Google DeepMind has released quantization-aware training (QAT) optimized versions of its Gemma 4 model family in GGUF Q4_0 format. The QAT versions preserve similar quality to bfloat16 while dramatically reducing memory requirements, with models available across the entire Gemma 4 lineup: E2B, E4B, 12B, 26B A4B, and 31B.