OpenAI

GPT-5-Mini

openai/gpt-5-mini
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-5-Mini是GPT-5的轻量级版本,专为高效处理日常推理任务而设计。它继承了 GPT-5的指令跟随能力和安全优化,同时具备更低的延迟和成本优势。

Input / output modalities
文本 · 图像 to 文本
Reference input / output price
Input¥1.75Output¥14per 1M tokens
Context window
400K
Added to catalog
Aug 15, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
OpenRouter¥1.75¥14¥0.18400K128K
Azure¥1.75¥14¥0.18400K128K

OpenRouter

Latency
6.18s
Throughput
29 tokens/s
Context
400K

Pricing

Input
¥1.75/M tokens
Output
¥14/M tokens
Cached
¥0.18/M tokens

Specifications

Context
400K
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Azure

Latency
2.17s
Throughput
137 tokens/s
Context
400K

Pricing

Input
¥1.75/M tokens
Output
¥14/M tokens
Cached
¥0.18/M tokens

Specifications

Context
400K
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-5-Mini 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-mini", 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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