OpenAI

O3 Deep Research

openai/o3-deep-research
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

(当前仅支持/v1/responses接口)OpenAI O3 Deep Reserch模型。

Input / output modalities
文本 · 图像 · 文件 to 文本
Reference input / output price
Input¥70Output¥280per 1M tokens
Context window
200K
Added to catalog
Nov 12, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Azure¥70¥280¥17.5200K100K

Azure

Latency
2.49s
Throughput
133 tokens/s
Context
200K

Pricing

Input
¥70/M tokens
Output
¥280/M tokens
Cached
¥17.5/M tokens

Additional pricing

Tools · Web search preview
¥0.07

Specifications

Context
200K
Max output
100K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

O3 Deep Research 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/o3-deep-research", 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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