OpenAI

GPT-4o-mini

openai/gpt-4o-mini
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-4o-mini是一款快速、经济的小型模型,适用于聚焦型任务和微调场景。它支持文本和图像输入,并生成文本输出,在成本和响应速度方面具有显著优势。

Input / output modalities
文本 · 图像 · 音频 to 文本
Reference input / output price
Input¥1.05Output¥4.2per 1M tokens
Context window
128K
Added to catalog
Apr 9, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
OpenRouter¥1.05¥4.2¥0.53128K16.4K
Azure¥1.05¥4.2¥0.53128K16.4K

OpenRouter

Latency
1.82s
Throughput
96 tokens/s
Context
128K

Pricing

Input
¥1.05/M tokens
Output
¥4.2/M tokens
Cached
¥0.53/M tokens

Specifications

Context
128K
Max output
16.4K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Azure

Latency
2.09s
Throughput
1 tokens/s
Context
128K

Pricing

Input
¥1.05/M tokens
Output
¥4.2/M tokens
Cached
¥0.53/M tokens

Specifications

Context
128K
Max output
16.4K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GPT-4o-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-4o-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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