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| last-modified | Wed, 30 Sep 2026 01:13:27 GMT |
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@
@@ -5,7 +5,7 @@ <atom:link href="https://aws.amazon.com/blogs/machine-learning/feed/" rel="self" type="application/rss+xml"/> <link>https://aws.amazon.com/blogs/machine-learning/</link> <description>Official Machine Learning Blog of Amazon Web Services</description>-
<lastBuildDate>Tue, 22 Sep 2026 18:10:22 +0000</lastBuildDate>+
<lastBuildDate>Wed, 30 Sep 2026 01:13:14 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod>@
@@ -13,63 +13,162 @@ 1 </sy:updateFrequency> <item>-
<title>Bring more intelligence to everyday work with GPT-6 Sol and GPT-6 Luna on Amazon Bedrock</title>-
<link>https://aws.amazon.com/blogs/machine-learning/bring-more-intelligence-to-everyday-work-with-gpt-6-sol-and-gpt-6-luna-on-amazon-bedrock/</link>+
<title>Amazon Bedrock expands Claude model availability to in-country inferencing in India</title>+
<link>https://aws.amazon.com/blogs/machine-learning/amazon-bedrock-expands-claude-model-availability-to-india-cross-region-inference/</link> -
<dc:creator><![CDATA[Tanvi Girinath]]></dc:creator>-
<pubDate>Tue, 22 Sep 2026 18:10:22 +0000</pubDate>+
<dc:creator><![CDATA[Aamna Najmi]]></dc:creator>+
<pubDate>Wed, 30 Sep 2026 01:13:14 +0000</pubDate> <category><![CDATA[Amazon Bedrock]]></category> <category><![CDATA[Announcements]]></category> <category><![CDATA[Intermediate (200)]]></category>-
<guid isPermaLink="false">f91c4872e4efa50ad6fa1a0161799f318feef3c1</guid>-
-
<description>GPT-6 Sol and GPT-6 Luna are now generally available on Amazon Bedrock, giving you more options to match intelligence and efficiency to each workload.</description>-
<content:encoded><p><em>GPT-6 Sol and GPT-6 Luna are now generally available on Amazon Bedrock, giving you more options to match intelligence and efficiency to each workload.</em></p>-
<p>The value of AI at scale depends on two dimensions: what a model can do and how often you can put it to use. Greater intelligence expands the complexity a model can handle, from subtle coding problems to multistep processes across tools. Efficiency determines how broadly that intelligence c…-
<p><a href="https://aws.amazon.com/blogs/machine-learning/take-on-your-most-ambitious-work-with-gpt-6-astra-on-amazon-bedrock/" target="_blank" rel="noopener">GPT-6 Astra</a> established the upper end of the GPT-6 family for the most ambitious projects, where achieving the highest-…-
<p>Today, <a href="https://aws.amazon.com/bedrock/openai/" target="_blank" rel="noopener">GPT-6 Sol and GPT-6 Luna</a> from OpenAI are generally available on <a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock</a>, running on an infer…-
<h2 id="solve-harder-problems-every-day">Solve harder problems every day</h2>-
<p>GPT-6 Sol is designed for demanding tasks that recur throughout development and operations. It can implement features, debug issues, refactor and review code, analyze data, and complete multistep processes across tools and applications. Improvements over GPT-5.6 Sol in coding and computer u…-
<p>As GPT-6 Sol handles more of that process, developers need to see what it changed, what it verified, and what it could not confirm. On an internal factuality evaluation, OpenAI found that GPT-6 Sol made approximately half as many factual mistakes as GPT-5.6 Sol. GPT-6 Sol also benefits from…-
<p>Together, stronger execution and clearer reporting make GPT-6 Sol practical across the development cycle. The relevant measure there is the total cost of reaching a usable result, including output quality, token usage, retries, and latency.</p>-
<h2 id="make-focused-intelligence-economical-at-volume">Make focused intelligence economical at volume</h2>-
<p>When a task runs thousands of times a day, the economics of each call determine whether the workflow scales. A single classification or summary is inexpensive on its own, but the cost of extraction, routing, and follow-up across a full document pipeline compounds with every additional reque…-
<p>GPT-6 Luna is designed for workloads where that volume matters. You can use it to extract information from large document collections, summarize incoming material, classify inputs, and answer focused questions across many users or applications.</p>-
<p>Efficiency at volume also requires consistent&nbsp;outputs. OpenAI’s evaluations show improvements in GPT-6 Luna’s factual reliability and clearer communication of results. You can also adjust reasoning effort per request to balance the quality, responsiveness, and cost each task requir…-
<h2 id="match-intelligence-to-each-step-without-rebuilding-context">Match intelligence to each step without rebuilding context</h2>-
<p>A single application may need different levels of intelligence as a request progresses. You might use GPT-6 Luna to classify incoming requests, GPT-6 Sol to investigate complex cases, and GPT-6 Astra when additional reasoning depth can materially change a decision. This concentrates intelli…-
<p>Within each stage, repeated calls to the same model may reuse instructions, tool definitions, policies, and reference material. Reprocessing that context can erode the efficiency gained by selecting the appropriate model.</p>-
<p>GPT-6 Sol and GPT-6 Luna support explicit prompt caching on Amazon Bedrock. You can mark prompt content for reuse, allowing subsequent requests to focus processing on new input. This is useful for coding assistants that reuse repository instructions, support applications grounded in the sam…-
<h2 id="run-gpt-6-at-scale-with-performance-and-control">Run GPT-6 at scale with performance and control</h2>-
<p>As AI usage grows, model quality is only part of what determines whether an application succeeds in production. Teams also need infrastructure that maintains performance as demand changes, economics that hold across repeated requests, and controls that protect sensitive data. Amazon Bedrock…-
<p>You can govern model access through AWS Identity and Access Management (IAM) policies and audit every invocation through AWS CloudTrail. Virtual private cloud (VPC) endpoints powered by AWS PrivateLink help keep traffic within your network boundaries. Inference runs on hardware-isolated inf…-
<p>Your inference data isn’t used for model training, and using GPT-6 Sol and GPT-6 Luna doesn’t require you to opt into sharing your data with OpenAI. For <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/abuse-detection.html" target="_blank" rel="noopener">automated abuse d…-
<h2 id="get-started">Get started</h2>-
<p>You can get started with GPT-6 Sol and GPT-6 Luna in the <a href="https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/" target="_blank" rel="noopener">Amazon Bedrock console</a> or programmatically through supported Amazon Bedrock APIs. For information about …-
<p><em>Interested in how Amazon Bedrock can support your team?</em> <em><a href="https://pages.awscloud.com/Amazon-Bedrock-Contact-Us.html" target="_blank" rel="noopener">Connect with us</a> to start the conversation.</em></p>+
<guid isPermaLink="false">5ee89c1b26308404d27592d483f282630a0d18d9</guid>+
+
<description>Anthropic's Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 are now available in India through Amazon Bedrock geographic cross-Region inference. You can access these models while processing data within the India Regions, and get started from the Amazon Bedrock console or with …+
<content:encoded><p>We’re excited to announce the availability of Anthropic’s <a href="https://aws.amazon.com/blogs/machine-learning/introducing-claude-opus-5-on-aws-anthropics-most-capable-opus-model/" target="_blank" rel="noopener">Claude Opus 5</a>, <a href="https:/…+
<p>In this post, we discuss how India geographic cross-Region inference works from the Mumbai and Hyderabad Regions on Amazon Bedrock for Anthropic Claude models. We also show how to get started from the Amazon Bedrock console and with code, using Anthropic’s Messages API, Amazon Bedrock Invok…+
<h2 id="india-geographic-cross-region-inference">India inference</h2>+
<p>To help you achieve the scale of your AI applications, Amazon Bedrock offers cross-Region inference profiles, a feature you can use to distribute inference across multiple AWS Regions without having to manage capacity in each Region. The request originates from your source Region where you …+
<h2 id="access-claude-models-from-the-amazon-bedrock-console">Access Claude models from the Amazon Bedrock console</h2>+
<p>You can access Claude models in the text playground in the Amazon Bedrock console, which requires no coding or SDK setup. You can send prompts, adjust inference parameters, and switch between variants to get a feel for each model before you integrate the API.</p>+
<ol type="1">+
<li>Open the <a href="https://console.aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock console</a> in the Region that you want to use as the source.</li>+
<li>In the navigation pane, under <strong>Test</strong>, choose <strong>Playground</strong>.</li>+
<li>Choose <strong>Select model</strong> in the middle of the page.</li>+
<li>Search for Anthropic Claude Opus 5, select <strong>IN Anthropic Claude Opus 5</strong> as the inference profile under <strong>Inference</strong>, and choose <strong>Apply</strong>.</li>+
<li>Enter a prompt and choose <strong>Run</strong> to generate a response.</li>+
</ol>+
<div style="width: 810px" class="wp-caption alignnone">+
<a href="images/image2.png" target="_blank" rel="noopener"><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/18/ML-21953-1.png" alt="The Claude Opus 5 model selected in the Amazon Bedrock console text playground" width="800"></a>+
<p class="wp-caption-text">Figure 1: The Claude Opus 5 model selected in the Amazon Bedrock console playground</p>+
</div>+
<h2 id="call-claude-models-with-the-anthropic-messages-api-and-amazon-bedrock-invokemodel-and-converse-api">Call Claude models with the Anthropic Messages API and Amazon Bedrock InvokeModel and Converse API</h2>+
<p>You can access Anthropic’s Claude Opus 5, Claude Sonnet 5, or Claude Haiku 4.5 programmatically with the India geographic inference profile ID using the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html" target="_blank" rel="noo…+
<h3 id="prerequisites">Prerequisites</h3>+
<ol type="1">+
<li>Active AWS account with Amazon Bedrock access.</li>+
<li>AWS CLI installed and configured.</li>+
<li>Python 3.8+.</li>+
<li>Boto3 installed: <code>pip install boto3</code>.</li>+
<li>Anthropic SDK installed: <code>pip install anthropic</code>.</li>+
<li>The Bedrock Token Generator for Amazon Bedrock model inference authentication installed: <code>pip install aws_bedrock_token_generator</code>.</li>+
<li><a href="https://aws.amazon.com/iam/" target="_blank" rel="noopener">AWS Identity and Access Management</a> (IAM) role or user has the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/geographic-cross-region-inference.html#geographic-cris-iam-setup" target="_bl…+
</ol>+
<p>Here’s a quick example using the AWS SDK for Python (Boto3) with the InvokeModel API:</p>+
<div class="hide-language">+
<pre><code class="language-python">import boto3+
import json+
+
# Create a Bedrock Runtime client+
bedrock_runtime = boto3.client(+
service_name="bedrock-runtime",+
region_name="ap-south-1"+
)+
+
# Invoke Claude Sonnet 5+
response = bedrock_runtime.invoke_model(+
modelId="in.anthropic.claude-sonnet-5",+
contentType="application/json",+
accept="application/json",+
body=json.dumps({+
"anthropic_version": "bedrock-2023-05-31",+
"max_tokens": 4096,+
"messages": [+
{+
"role": "user",+
"content": " Can you explain the features of Amazon Bedrock? "+
}+
]+
})+
)+
+
result = json.loads(response["body"].read())+
print(result["content"][0]["text"])</code></pre>+
</div>+
<p>You can also use the Amazon Bedrock Converse API for a unified multi-model experience:</p>+
<div class="hide-language">+
<pre><code class="language-python">import boto3+
+
# Create a Bedrock Runtime client+
bedrock_runtime = boto3.client(+
service_name="bedrock-runtime",+
region_name="ap-south-1"+
)+
+
# Invoke Claude Opus 5+
response = bedrock_runtime.converse(+
modelId="in.anthropic.claude-opus-5",+
messages=[+
{+
"role": "user",+
"content": [+
{+
"text": " Can you explain the features of Amazon Bedrock?"+
}+
]+
}+
],+
inferenceConfig={+
"maxTokens": 4096+
}+
)+
+
if 'output' in response:+
blocks = response['output']['message']['content']+
print('\n'.join(b.get('text', '') for b in blocks if 'text' in b))</code></pre>+
</div>+
<p>You can also use the Anthropic Messages API using the <code>anthropic</code> SDK package for a streamlined experience:</p>+
<div class="hide-language">+
<pre><code class="language-python">from anthropic import Anthropic+
from aws_bedrock_token_generator import provide_token+
+
token = provide_token(region="ap-south-1")+
+
client = Anthropic(+
base_url="https://bedrock-runtime.ap-south-1.amazonaws.com/anthropic",+
api_key=token,+
)+
+
response = client.messages.create(+
model="in. anthropic.claude-haiku-4-5-20251001-v1:0",+
max_tokens=1024,+
messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}],+
)+
+
print(response)</code></pre>+
</div>+
<p>You can monitor usage, performance, and costs through <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/monitoring.html" target="_blank" rel="noopener">CloudWatch</a> and <a href="https://aws.amazon.com/aws-cost-management/aws-cost-explorer/" target="_blank" rel="…+
<h2 id="conclusion">Conclusion</h2>+
<p>With the launch of Anthropic’s Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 using Amazon Bedrock with India geographic cross-Region inference, you can now build highly scalable, resilient generative AI applications while keeping inference within the country. To get started, access A…<hr style="width: 100%"><h2>About the authors</h2><footer> <div class="blog-author-box" style="padding-top: 2.0em"> <div class="blog-author-image" style="margin-right: 1.0em">-
<p><img class="alignnone size-full wp-image-139734" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/22/ML-21636-6.jpg" alt="" width="300" height="400"></p>+
<p><img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/18/ML-21953-2.jpg" alt="Aamna Najmi" width="100" height="133"></p> </div>-
<h3 class="lb-h4">Tanvi Girinath</h3>-
<p>Tanvi is a Product Marketing Manager for Amazon Bedrock at Amazon Web Services (AWS), where she helps customers adopt and scale AI applications and agents with Amazon Bedrock.</p>+
<h3 class="lb-h4">Aamna Najmi</h3>+
<p>Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with Amazon… </div> <div class="blog-author-box" style="padding-top: 2.0em"> <div class="blog-author-image" style="margin-right: 1.0em">-
<p><img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/21/ML-21956-2.jpg" alt="Chris Dickens" width="100" height="133"></p>+
<p><img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/18/ML-21953-3.jpg" alt="Eugenio Soltero" width="100" height="133"></p> </div>-
<h3 class="lb-h4">Chris Dickens</h3>-
<p>Chris is a Member of Product Staff at OpenAI focused on the OpenAI APIs. His work includes collaboration with AWS on Amazon Bedrock to make OpenAI’s frontier models widely accessible to developers.</p>+
<h3 class="lb-h4">Eugenio Soltero</h3>+
<p>Eugenio is a Sr.&nbsp;Product Marketing Manager for Amazon Bedrock at AWS. With several years of experience in generative AI, he helps customers navigate the evolving landscape of foundation models and generative AI to adopt solutions that deliver measurable value.</p> </div> <div class="blog-author-box" style="padding-top: 2.0em"> <div class="blog-author-image" style="margin-right: 1.0em">-
<img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/03/ML-21849-3.jpg" alt="Manish Rathaur" width="100" height="133">+
<p><img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/18/ML-21953-4.jpg" alt="Sofian Hamiti" width="100" height="133"></p> </div>-
<h3 class="lb-h4">Manish Rathaur</h3>-
<p style="overflow: hidden">Manish is a Senior Product Manager for Amazon Bedrock.</p>+
<h3 class="lb-h4">Sofian Hamiti</h3>+
<p>Sofian is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate about empowering diverse talents to drive global impact and achieve their career aspirations.</p>+
</div>+
<div class="blog-author-box" style="padding-top: 2.0em">+
<div class="blog-author-image" style="margin-right: 1.0em">+
<p><img loading="lazy" class="alignnone size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/29/ML-21953-5.png" alt="Ayan Ray" width="100" height="133"></p>+
</div>+
<h3 class="lb-h4">Ayan Ray</h3>+
<p>Ayan is a Principal Partner Solutions Architect and AI Tech Lead at AWS, serving as the Worldwide Tech Lead for Anthropic at AWS. He works at the intersection of cloud architecture and Artificial Intelligence, helping organizations adopt and scale Anthropic’s technologies on AWS.</p>… </div></footer></content:encoded> @
@@ -77,61 +176,59 @@ </item> <item>-
<title>Claude Opus 5.5 is now available on AWS</title>-
<link>https://aws.amazon.com/blogs/machine-learning/claude-opus-5-5-is-now-available-on-aws/</link>+
<title>Introducing Anthropic models on Amazon Bedrock for in-region inference in Seoul and Singapore</title>+
<link>https://aws.amazon.com/blogs/machine-learning/introducing-anthropic-models-on-amazon-bedrock-for-in-region-inference-in-seoul-and-singapore/</link> <dc:creator><![CDATA[Aamna Najmi]]></dc:creator>-
<pubDate>Tue, 22 Sep 2026 17:28:01 +0000</pubDate>+
<pubDate>Wed, 30 Sep 2026 01:13:12 +0000</pubDate> <category><![CDATA[Amazon Bedrock]]></category> <category><![CDATA[Announcements]]></category> <category><![CDATA[Intermediate (200)]]></category>-
<guid isPermaLink="false">2e3d91b641ca237ade2f2527a383a24847fe5ded</guid>-
-
<description>Claude Opus 5.5, Anthropic's most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what's new in Opus 5.5, practical guidance, and how to start building with the model on A…-
<content:encoded><p>Today, we’re excited to announce the availability of Claude Opus 5.5 on <a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock</a> and <a href="https://aws.amazon.com/blogs/machine-learning/introducing-claude-platfor…-
<p>This post covers Claude Opus 5.5’s improvements, practical guidance, and how to start building with the model on Amazon Bedrock.</p>-
<h2 id="what-makes-claude-opus-5.5-different">What makes Claude Opus 5.5 different</h2>-
<p>According to Anthropic, Claude Opus 5.5 does more with fewer tokens than Claude Opus 5, and new pricing passes those gains straight to customers. Lower per-token prices and much cheaper cache reads stack on top of the efficiency gains. The result is an average lower cost per task than Claud…-
<p>Claude Opus 5.5 is trained to communicate more clearly. As it works, it surfaces what it did, what it found, and what it needs, making long-running tasks easier to follow. Adaptive thinking is always on, and Opus 5.5 decides how much reasoning each task needs. You can use effort as your con…-
<p>Claude Opus 5.5 is the first Opus model that comes with safety classifiers similar to Claude Fable 5.1 in biology, cyber security, and AI development. Requests will be refused more frequently as compared to previous Opus versions.</p>-
<h2 id="use-cases">Use cases</h2>-
<p>Claude Opus 5.5 capabilities are a good fit for industries where consistency and depth matter most. In software development, Opus 5.5 is an improvement over Opus 5 for longer-running sessions with clear communication and explainability, making it easier to use, review, and trust. For knowle…-
<h2 id="getting-started-with-claude-opus-5.5-on-amazon-bedrock">Getting started with Claude Opus 5.5 on Amazon Bedrock</h2>-
<p>To try Claude Opus 5.5, open the <a href="https://console.aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock console</a>, choose <strong>Test</strong>, then <strong>Playground</strong>, and select <strong>Claude Opus 5.5</stro…-
<div id="attachment_140005" style="width: 1876px" class="wp-caption alignnone">-
<img aria-describedby="caption-attachment-140005" loading="lazy" class="size-full wp-image-140005" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/22/Screenshot-2026-09-22-at-18.37.23.png" alt="" width="1866" height="986">-
<p id="caption-attachment-140005" class="wp-caption-text">Figure 1: Selecting an Anthropic Claude model in the Amazon Bedrock console Playground</p>-
</div>-
<div class="mceTemp">-
<div id="attachment_140006" style="width: 1627px" class="wp-caption alignnone">-
<img aria-describedby="caption-attachment-140006" loading="lazy" class="size-full wp-image-140006" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/22/Screenshot-2026-09-22-at-18.36.48.png" alt="" width="1617" height="906">-
<p id="caption-attachment-140006" class="wp-caption-text">Figure 2: Running a prompt against a Claude model in the Amazon Bedrock console Playground</p>-
</div>-
<p>Programmatically, you can call the model with the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html" target="_blank" rel="noopener">Anthropic Messages API</a> against <code>bedrock-runtime</code> and <co…-
<h3 id="prerequisites">Prerequisites</h3>-
<ol type="1">-
<li>Active AWS account with Amazon Bedrock access.</li>-
<li>AWS Command Line Interface (AWS CLI) installed and configured.</li>-
<li>Python 3.10+.</li>-
<li>Boto3 installed: <code>pip install boto3</code>.</li>-
<li>Anthropic SDK installed: <code>pip install anthropic</code>.</li>-
<li>The Amazon Bedrock Token Generator for Amazon Bedrock authentication installed: <code>pip install aws_bedrock_token_generator</code>.</li>-
<li>AWS Identity and Access Management (IAM) permissions: <code>bedrock:InvokeModel</code> and <code>bedrock:InvokeModelWithResponseStream</code>.</li>-
</ol>-
<p>Here’s a quick example using the AWS SDK for Python (Boto3):</p>-
<div class="hide-language">-
<pre><code class="language-python">import boto3+
<guid isPermaLink="false">6e28f69c4f4137454e595f46fee566a1249a74da</guid>+
+
<description>Amazon Bedrock now supports Anthropic's Claude Opus 5 and Claude Sonnet 5 with in-region inference in Seoul, and Claude Sonnet 5 in Singapore. If you have local data processing requirements in South Korea or Singapore, you can now use these Anthropic models at scale, with inference…+
<content:encoded><p>Amazon Bedrock now supports the Anthropic Claude models: <a href="https://aws.amazon.com/blogs/machine-learning/introducing-claude-opus-5-on-aws-anthropics-most-capable-opus-model/" target="_blank" rel="noopener">Claude Opus 5</a> and <a href="https…+
<p>In this post, we walk through how in-region inference works from the Asia Pacific (Seoul) Region (<code>ap-northeast-2</code>) and Asia Pacific (Singapore) Region (<code>ap-southeast-1</code>) using the <code>bedrock-runtime</code> endpoint. We also show …+
<h2 id="in-region-inference">In-region inference</h2>+
<p>To help you meet strict data residency requirements for your AI applications, Amazon Bedrock offers in-region inference. Your request is processed entirely within the single AWS Region you specify, and it does not leave that Region. Use this when you have a need for strict single-Region dat…+
<p>Unlike cross-Region inference profiles, there is no routing layer. The request you send to the Seoul (<code>ap-northeast-2</code>) or Singapore (<code>ap-southeast-1</code>) Region is served by that Region alone. Your input prompts and output results stay within it f…+
<h2 id="access-claude-models-from-the-amazon-bedrock-console">Access Claude models from the Amazon Bedrock console</h2>+
<p>You can access Claude models in the text playground in the Amazon Bedrock console, which requires no coding or SDK setup. You can send prompts, adjust inference parameters, and switch between variants to get a feel for each model before you integrate the API.</p>+
<ol type="1">+
<li>Open the <a href="https://console.aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock console</a> in a Region that you want to use as a source.</li>+
<li>In the navigation pane, under <strong>Test</strong>, choose <strong>Playground</strong>.</li>+
<li>Choose <strong>Select model</strong> in the middle of the page.</li>+
<li>Search for <code>anthropic.claude-opus-5</code>, select <strong>On-Demand</strong> under <strong>Inference</strong>, and choose <strong>Apply</strong>.</li>+
<li>Enter a prompt and choose <strong>Run</strong> to generate a response.</li>+
</ol>+
<div style="width: 810px" class="wp-caption alignnone">+
<a href="images/image1.png" target="_blank" rel="noopener"><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/09/29/ML-22088-1.png" alt="Anthropic Claude Opus 5 model selected in the Amazon Bedrock console playground" width="800"></a>+
<p class="wp-caption-text">Figure 1: The Anthropic Opus 5 model selected in the Amazon Bedrock console playground with in-region inference</p>+
</div>+
<h2 id="call-claude-models-with-the-anthropic-messages-api-and-amazon-bedrock-invokemodel-and-converse-api">Call Claude models with the Anthropic Messages API and Amazon Bedrock InvokeModel and Converse API</h2>+
<p>You can access Anthropic’s Claude Opus 5 or Claude Sonnet 5 programmatically with Seoul in-region inference using the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html?trk=d8ec3b19-0f37-4f8c-8c12-189f913e205c&amp;sc_channel=…+
<h3 id="prerequisites">Prerequisites</h3>+
<ol type="1">+
<li>Active AWS account with Amazon Bedrock access.</li>+
<li>AWS CLI installed and configured.</li>+
<li>Python 3.8+.</li>+
<li>Boto3 installed: <code>pip install boto3</code>.</li>+
<li>Anthropic SDK installed: <code>pip install anthropic</code>.</li>+
<li>The Amazon Bedrock Token Generator for Amazon Bedrock authentication installed: <code>pip install aws_bedrock_token_generator</code>.</li>+
</ol>+
<p>Here’s a quick example using the AWS SDK for Python (Boto3) with the InvokeModel API:</p>+
<div class="hide-language">+
<pre><code class="language-python">import boto3import json-
# Create a Bedrock Runtime client+
# Create a Bedrock Runtime client with in-region inference in Singaporebedrock_runtime = boto3.client( service_name="bedrock-runtime",-
region_name="us-east-1"-
)+
region_name="ap-southeast-1")-
# Invoke Claude Opus 5.5+
# Invoke Claude Sonnet 5response = bedrock_runtime.invoke_model(-
modelId="global.anthropic.claude-opus-5-5",+
modelId="anthropic.claude-sonnet-5", contentType="application/json", accept="application/json", body=json.dumps({@
@@ -140,38 +237,34 @@ response = bedrock_runtime.invoke_model( "messages": [ { "role": "user",-
"content": "An S3 bucket serves 40 TB/month egress. Estimate the monthly egress cost at $0.09/GB, and state one architecture change to cut it. Show the calculation, keep it under 120 words."+
"content": " Can you explain the features of Amazon Bedrock? " } ] }))result = json.loads(response["body"].read())-
# Opus 5.5 is a reasoning model: the response may include a thinking block-
# before the text block, so select the text block rather than a fixed index.-
print(next(b["text"] for b in result["content"] if b["type"] == "text"))</code></pre>-
</div>-
<p>You can also use the Amazon Bedrock Converse API for a unified multi-model experience:</p>-
<div class="hide-language">-
<pre><code class="language-python">import boto3+
print(result["content"][0]["text"])</code></pre>+
</div>+
<p>You can also use the Amazon Bedrock Converse API for a unified multi-model experience:</p>+
<div class="hide-language">+
<pre><code class="language-python">import boto3-
# Create a Bedrock Runtime client+
# Create a Bedrock Runtime client with in-region inference in Seoulbedrock_runtime = boto3.client( service_name="bedrock-runtime",-
region_name="us-east-1"+
region_name="ap-northeast-2")-
# Invoke Claude Opus 5.5+
# Invoke Claude Opus 5response = bedrock_runtime.converse(-
modelId="global.anthropic.claude-opus-5-5",+
modelId="anthropic.claude-opus-5", messages=[-
{-
"role": "user",-
"content": [-
{-
"text": "Can you explain the features of Amazon Bedrock?"-
}-
]+
{ "role": "user",+
"content": [+
{ "text": " Can you explain the features of Amazon Bedrock?"+
}+
] } ], inferenceConfig={@
@@ -182,383 +275,353 @@ response = bedrock_runtime.converse(if 'output' in response: blocks = response['output']['message']['content'] print('\n'.join(b.get('text', '') for b in blocks if 'text' in b))</code></pre>-
</div>-
<p>You can also use the Anthropic Messages API through the <code>anthropic</code> SDK package for a streamlined experience:</p>-
<div class="hide-language">-
<pre><code class="language-python">from anthropic import Anthropic+
</div>+
<p>You can also use the Anthropic Messages API through the <code>anthropic</code> SDK package for a streamlined experience:</p>+
<div class="hide-language">+
<pre><code class="language-python">from anthropic import Anthropicfrom aws_bedrock_token_generator import provide_token-
token = provide_token(region="us-east-1")+
token = provide_token(region="ap-northeast-2")client = Anthropic(-
base_url="https://bedrock-runtime.us-east-1.amazonaws.com/anthropic",+
base_url="https://bedrock-runtime.ap-northeast-2.amazonaws.com/anthropic", api_key=token,)-
# Invoke Claude Opus 5.5response = client.messages.create(-
model="global.anthropic.claude-opus-5-5",+
model="anthropic.claude-sonnet-5", max_tokens=1024, messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}],)+
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