xAI’s Grok 4.6 is now available in Amazon Bedrock
xAI's Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support.
xAI has made its Grok 4.6 model available on Amazon Bedrock, expanding the frontier model's capabilities for long-running agents, coding, and knowledge work. Grok 4.6 launched on Bedrock on August 18, 2026, and provides a 500K token context window, along with four reasoning effort levels: low, medium, high, and xhigh. This is xAI's second model in Amazon Bedrock.
Grok 4.6 enhances the existing surface area by being available on both the bedrock-mantle and bedrock-runtime endpoints, supporting the Converse API alongside Chat Completions and Responses. The model is designed for complex tasks that span multiple steps, such as researching a topic, analyzing information, working across a codebase, or turning an idea into a polished application or work artifact.
xAI trained Grok 4.6 using a longer supplemental training run, with a focus on reasonings and advanced technical concepts, high-quality engineering data, and improved optimizer and training recipe. The model was then trained on various agentic reinforcement learning tasks, including knowledge work, general coding, and domain-specific environments such as kernel optimization, web development, and computer-aided design.
Several safety improvements have been made to Grok 4.6, including better safeguards that have been calibrated in line with the model's capabilities. The model has undergone a wide range of pre-deployment testing for capabilities and safeguard calibration, as well as post-deployment and third-party testing. This safety stack aims to maximize utility and security across legitimate use cases in areas like vulnerability patching, accelerating the engineering design cycle, and augmenting AI research.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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