Today, AWS announced the availability of Gemma 4 E4B, Gemma 4 26B-A4B, and Gemma 4 31B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three instruction-tuned models from Google DeepMind bring multimodal capabilities with configurable reasoning, native function calling, and multilingual support across 140+ languages, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure. All three models share a common set of capabilities that address a broad range of enterprise AI use cases: Thinking - Built-in reasoning mode that lets the model think step-by-step before answering Image Understanding - Object detection, document and PDF parsing, screen and UI understanding, chart comprehension, OCR including multilingual, and handwriting recognition Video Understanding - Analyze video content by processing sequences of frames Interleaved Multimodal Input - Freely mix text and images in any order within a single prompt Function Calling - Native support for structured tool use, enabling agentic workflows Coding - Code generation, completion, and correction Multilingual - Out-of-the-box support for 35+ languages, pre-trained on 140+ languages Customers can choose the model that best fits their workload: Gemma 4 E4B additionally supports audio input for automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.