OpenAI

GPT-5.4-Pro

openai/gpt-5.4-pro
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-5 Pro 是 OpenAI 最先进的模型,在推理、代码质量和用户体验方面进行了重大改进。它针对需要分步推理、遵循指令和高风险用例准确性的复杂任务进行了优化。它支持测试时路由功能和高级提示理解,包括用户指定的意图,例如“认真考虑这个问题”。改进包括减少幻觉、阿谀奉承,以及在编码、写作和健康相关任务中表现更好。

Input / output modalities
文本 · 图像 · 文件 to 文本
Reference input / output price
Input¥210Output¥1260per 1M tokens
Context window
1.1M
Added to catalog
Mar 10, 2026

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Azure≤ 272K¥210¥1260¥01.1M128K
> 272K¥420—¥0
≥ 272K—¥1890—

Azure

Latency
1.5s
Throughput
9 tokens/s
Context
1.1M

Pricing

Input
¥210/M tokens
Output
¥1260/M tokens
Cached
¥0/M tokens

Tiered pricing

Pricing varies by input token range.

0–272K Token

Input tier
¥210/M tokens
Output tier
¥1260/M tokens
Cached tier
¥0/M tokens

272K–∞ Token

Input tier
¥420/M tokens
Output tier
¥1890/M tokens
Cached tier
¥0/M tokens

Specifications

Context
1.1M
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-5.4-Pro 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="openai/gpt-5.4-pro", 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}")
                
              

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