Anthropic

Claude Opus 5.5

anthropic/claude-opus-5.5
/v1/chat/completions/v1/messages/v1/responses/v1/completions/v1beta/models/*/v1/models/*

Claude Opus 5.5 是 Anthropic 推出的旗舰模型,用于高难度推理、代码开发以及长周期智能体任务,为 Claude Opus 5 的迭代版本。该模型在大型代码库的多步修改、代码审查与漏洞查找、金融和科学分析,以及读取信息密集的图表、示意图和截图方面表现尤为突出。相比前代产品,它更加严谨,仅陈述可佐证的数据并标注对应的信息来源。

Input / output modalities
Not provided to Not provided
Reference input / output price
Input¥28Output¥140per 1M tokens
Context window
1M
Added to catalog
Sep 23, 2026

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Amazon Bedrock¥28¥140¥1.41M128K

Amazon Bedrock

Latency
5.42s
Throughput
121 tokens/s
Context
1M

Pricing

Input
¥28/M tokens
Output
¥140/M tokens
Cached
¥1.4/M tokens
Cache write (1 hour)
¥56/M tokens
Cache write (5 minutes)
¥35/M tokens

Additional pricing

Cache write
¥35
Cache read
¥1.4
Cache write price 5m
¥35
Cache write price 1h
¥56

Specifications

Context
1M
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1/completions/v1beta/models/*/v1/models/*

Claude Opus 5.5 code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="anthropic/claude-opus-5.5", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

Related Models