Google

Gemini 2.5 Pro

google/gemini-2.5-pro
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Gemini 2.5 Pro是一款强大的推理模型,专为解决复杂问题而设计。它具备卓越的理解与分析能力,能够处理来自多种信息源的海量数据,包括文本、音频、图像、视频,甚至是完整的代码库。

Input / output modalities
文本 · 图像 · 文件 · 视频 · 音频 to 文本
Reference input / output price
Input¥8.75Output¥70per 1M tokens
Context window
1M
Added to catalog
Jun 18, 2025

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Google Vertex≤ 200K¥8.75¥70¥0.881M65.5K
≥ 200K¥17.5¥105¥1.75
OpenRouter≤ 200K¥8.75¥70¥0.881M65.5K
≥ 200K¥17.5¥105¥1.75

Google Vertex

Latency
34.87s
Throughput
749 tokens/s
Context
1M

Pricing

Input
¥8.75/M tokens
Output
¥70/M tokens
Cached
¥0.88/M tokens

Tiered pricing

Pricing varies by input token range.

0–200K Token

Input tier
¥8.75/M tokens
Output tier
¥70/M tokens
Cached tier
¥0.88/M tokens

200K–∞ Token

Input tier
¥17.5/M tokens
Output tier
¥105/M tokens
Cached tier
¥1.75/M tokens

Specifications

Context
1M
Max output
65.5K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

OpenRouter

Latency
6.93s
Throughput
175 tokens/s
Context
1M

Pricing

Input
¥8.75/M tokens
Output
¥70/M tokens
Cached
¥0.88/M tokens

Tiered pricing

Pricing varies by input token range.

0–200K Token

Input tier
¥8.75/M tokens
Output tier
¥70/M tokens
Cached tier
¥0.88/M tokens

200K–∞ Token

Input tier
¥17.5/M tokens
Output tier
¥105/M tokens
Cached tier
¥1.75/M tokens

Specifications

Context
1M
Max output
65.5K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Gemini 2.5 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="google/gemini-2.5-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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