xAI’s Grok 4.6 is now available in Amazon Bedrock

來源原標題

xAI’s Grok 4.6 is now available in Amazon Bedrock

作者

Suheel Farooq

原始標籤

Amazon Bedrock · Amazon Machine Learning · Announcements · Artificial Intelligence · Foundation models · Generative AI · Intermediate (200) · Launch

原始連結

https://aws.amazon.com/blogs/machine-learning/xais-grok-4-6-is-now-available-in-amazon-bedrock/

摘要原文

Today, we are announcing that xAI’s Grok 4.6 is available in Amazon Bedrock , adding a frontier model built for long-running agents, coding, and knowledge work to the Bedrock model catalog. Grok 4.6 launched on Bedrock on August 18, 2026. It offers a 500K token context window and supports configurable reasoning effort at four levels: low, medium, high, and xhigh. This is xAI’s second model in Amazon Bedrock. When Grok 4.3 became generally available , xAI joined Amazon Bedrock as a model provider and the model was reachable through Bedrock Mantle, the OpenAI-compatible inference engine in Amazon Bedrock. Grok 4.6 widens that surface area considerably: it is available on both the bedrock-mantle and bedrock-runtime endpoints, and it supports the Converse API alongside Chat Completions and Responses. This post covers what xAI says Grok 4.6 is designed for, how it is packaged on Amazon Bedrock, and how to send your first request. The capability and training details in this section come from xAI’s launch announcement, Introducing Grok 4.6 . Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work. xAI describes the model as staying with complex tasks across many steps, whether that is researching a topic, analyzing information, working across a code base, or turning an idea into a polished application or work artifact. On training, xAI reports a longer supplemental training run than Grok 4.5, using curated model-generated data for reasoning and advanced technical concepts, high-quality engineering data, and an improved optimizer and training recipe. It then used Grok 4.5 to regenerate the supervised fine-tuning trajectories across reasoning efforts, agent harnesses, and domains including STEM, software engineering, and knowledge work, filtering out problematic traces with model-based checks. The model was then trained on a wide range of agentic reinforcement learning tasks spanning knowledge work, general coding, and domain-specific environments such as kernel optimization, web development, and computer-aided design. Two behaviors xAI calls out are worth noting for anyone building agents. On longer trajectories, the model began showing more self-testing and verification, checking its own work before moving on. It also produces stronger first passes on visual and interactive projects, establishing the structure and visual language of an application in a single pass, which the team found useful where the fastest route to a good result was to start with something substantial and then iterate. On safety, xAI states that Grok 4.6’s safeguards have been improved and calibrated in line with the model’s capabilities, backed by what it describes as its widest-ever suite of pre-deployment testing for capabilities and safeguard calibration, plus post-deployment and third-party testing. The company positions its safety stack as maximizing utility and security across legitimate use cases in domains such as vulnerability patching, accelerating the engineering design cycle, and augmenting AI research. xAI reports that Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. These are the figures it published for Grok 4.6 High at launch on August 12, 2026: Source: xAI, according to https://x.ai/news/grok-4-6 . Several of those evaluations come from Artificial Analysis, so it helps to know what they measure. According to Artificial Analysis , the Artificial Analysis Intelligence Index v4.1.1 is a composite that incorporates nine evaluations: GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA-Omniscience, and AA-LCR. Those cover agentic tool use, reasoning and knowledge, knowledge reliability, long context reasoning, and quantitative analysis over spreadsheets and documents. AA-Briefcase is its agentic knowledge work benchmark, where AA-Briefcase Elo aggregates rubric pass rate, analytical quality Elo, and presentation Elo, with higher scores better. Artificial Analysis also tracks cost and latency alongside intelligence. Its cost-per-task metric is a weighted average cost per Intelligence Index task, derived from input, cache hit, cache write, reasoning, and answer token prices, which is a useful lens if you are sizing a reasoning-heavy agent workload where reasoning tokens are a real line item. Several Bedrock capabilities are new for this model rather than carried over from the earlier Grok launch. The bedrock-runtime endpoint. Grok 4.6 is served on bedrock-runtime in addition to bedrock-mantle, so you can reach it with the AWS SDKs and the standard Bedrock control surface rather than only an OpenAI-compatible client. The Converse API, including streaming. Both converse and converse_stream are available. This is the practical payoff of runtime support: one message shape across models, and streaming through the usual Converse events ( messageStart , contentBlockDelta , contentBlockStop , messageStop , metadata ) without hand-rolling server-sent events (SSE) parsing. An xhigh reasoning effort level. Effort runs low, medium, high, xhigh, extending the range at the top end for problems where a deeper pass is worth the tokens. On Converse, set it through additionalModelRequestFields={"reasoning_effort": "xhigh"} rather than a reasoning parameter. Cross-Region inference. On bedrock-runtime you route through one of two inference profiles rather than pinning to a single Region. us.xai.grok-4.6 keeps traffic within the US geography when you have data residency requirements, and global.xai.grok-4.6 routes worldwide for the widest capacity pool. Global is also the cheaper of the two, at $2.00 per million input tokens against $2.20, so absent a residency constraint it is usually the better default. Amazon Bedrock Guardrails. Grok 4.6 now supports Guardrails on bedrock-runtime across its APIs, giving you content filters, denied topics, personally identifiable information (PII) redaction, and word policies. You attach a guardrail by ID and version on the request, and the policy is evaluated against both the prompt and the model’s response. For agentic workloads this matters because it puts a consistent policy boundary around a model that might run unattended across many steps. Invocation logging. With model invocation logging enabled, Grok 4.6 calls are captured as complete Amazon CloudWatch records: request body, response body, token counts including reasoning tokens, and the inference profile used. Useful for auditing agent runs where you need to see what the model was actually asked. Prompt caching. Cached input is billed at roughly a quarter of the standard input rate, which matters for agents that resend a large system prompt or document on every turn. Caching applies to a repeated prefix, so keep stable content at the front of the request, and read the cached token count in the usage block to confirm the discount is landing before you build it into a cost model. Tool calling, structured output, image input, response streaming, and encrypted reasoning content are available as well, but those date from the Grok 4.3 launch and are covered in that post. Grok 4.6 accepts text and image input and returns text. Audio, speech, video, and embedding modalities are not supported, and it does not generate images. The model is reachable through two endpoints, and the model ID differs depending on which one you use: On the API side, Grok 4.6 supports the Responses API, the Chat Completions API, and the Converse API. The Invoke API is not supported. Feature support differs by endpoint, which is the detail most likely to shape your integration choice: On bedrock-mantle, supported features include client-side tool calling, reasoning, structured outputs, prompt caching, response streaming, projects, and abuse detection.