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谷歌

Gemini 4 Hydrogen

google/gemini-4-hydrogen
/v1/chat/completions/v1/messages/v1beta/models/*/v1/models/*

Gemini 4 Hydrogen是谷歌Gemini 4系列中的高效主力模型,专为高吞吐量智能体工作流、代码编写与多模型路由场景打造,相比Gemini 4 Carbon,它成本更低、延迟更小。该模型可接收文本、图像、音频、视频以及文件输入,支持通过接续生成实现最高100万输出token的长解码能力,并可通过低、中、高、超高四档推理强度来控制思考深度。

Input / output modalities
文本 · 图像 · 文件 · 视频 · 音频 to 文本
Reference input / output price
Input¥10.5Output¥52.5per 1M tokens
Context window
1M
Added to catalog
Nov 13, 2026

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Google Vertex¥10.5¥52.5¥0.531M1M
OpenRouter¥10.5¥52.5¥0.531M1M

Google Vertex

Latency
10.59s
Throughput
52 tokens/s
Context
1M

Pricing

Input
¥10.5/M tokens
Output
¥52.5/M tokens
Cached
¥0.53/M tokens

Specifications

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

OpenRouter

Latency
10.59s
Throughput
52 tokens/s
Context
1M

Pricing

Input
¥10.5/M tokens
Output
¥52.5/M tokens
Cached
¥0.53/M tokens

Specifications

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

Gemini 4 Hydrogen 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="google/gemini-4-hydrogen", 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}")
                
              

No public applications are currently listed for this model.

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No related models are currently available.