AWS 推出 Claude Opus 5：Anthropic 最强 Opus 模型上线 Amazon Bedrock

英文标题：Introducing Claude Opus 5 on AWS: Anthropic’s most capable Opus model

AWS 宣布 Claude Opus 5 已在 Amazon Bedrock 和 Claude Platform on AWS 上线，这是 Anthropic 第五代 Claude 中首个 Opus 模型。Anthropic 表示，该模型在 Agentic 编程、知识工作、视觉理解和长时任务方面显著提升，并以 Opus 层级价格在多项领域达到 Claude Fable 5 的顶尖智能水平。Amazon Bedrock 默认提供零数据保留（ZDR），使企业能够在既有 AWS 环境中兼顾模型能力、数据治理、区域数据驻留与安全扩展。开发者可通过 Boto3、Converse API、Anthropic Messages API 及 AWS SDK 接入，并可在对话中动态增删工具。

1.3 MB 101.1 KB 124.1 KB 345.0 KB 141.2 KB 346.5 KB 600.8 KB 374.0 KB 51.4 KB 52.6 KB 109.9 KB 58.0 KB 57.4 KB 25.4 KB 270.2 KB

import boto3
import json

# Create a Bedrock Runtime client
bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name="us-east-1"
)

# Invoke Claude Opus 5
response = bedrock_runtime.invoke_model(
    modelId="global.anthropic.claude-opus-5",
    contentType="application/json",
    accept="application/json",
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
            }
        ]
    })
)

result = json.loads(response["body"].read())
print(result["content"][1]["text"])

import boto3

bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

response = bedrock_runtime.converse(
    modelId="global.anthropic.claude-opus-5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
                }
            ]
        }
    ],
    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))

from anthropic import AnthropicBedrockMantle

# Initialize the Bedrock Mantle client (uses SigV4 auth automatically)
mantle_client = AnthropicBedrockMantle(aws_region="us-east-1")

# Create a message using the Messages API
message = mantle_client.messages.create(
    model="anthropic.claude-opus-5",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions"}
    ]
)

print(message.content[1].text)

import boto3
import json

# Create a Bedrock Runtime client
bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name="us-east-1"
)

# Invoke Claude Opus 5
response = bedrock_runtime.invoke_model(
    modelId="global.anthropic.claude-opus-5",
    contentType="application/json",
    accept="application/json",
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
            }
        ]
    })
)

result = json.loads(response["body"].read())
print(result["content"][1]["text"])

import boto3

bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

response = bedrock_runtime.converse(
    modelId="global.anthropic.claude-opus-5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
                }
            ]
        }
    ],
    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))

from anthropic import AnthropicBedrockMantle

# Initialize the Bedrock Mantle client (uses SigV4 auth automatically)
mantle_client = AnthropicBedrockMantle(aws_region="us-east-1")

# Create a message using the Messages API
message = mantle_client.messages.create(
    model="anthropic.claude-opus-5",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions"}
    ]
)

print(message.content[1].text)