Google

Gemini 3.1 Flash Lite Preview

google/gemini-3.1-flash-lite-preview
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Gemini 3.1 Flash-Lite 是谷歌最具成本效益的 Gemini 型号,针对低延迟、高成本的大型语言模型流量进行了优化。它相比 Gemini 2.0/2.5 Flash-Lite 型号实现了显著的质量提升,在关键能力领域的性能与 Gemini 2.5 闪存相当。

Input / output modalities
文本 · 图像 · 文件 · 视频 · 音频 to 文本
Reference input / output price
Input¥1.75Output¥10.5per 1M tokens
Context window
1M
Added to catalog
Mar 4, 2026

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Google Vertex¥1.75¥10.5¥0.181M65.5K
OpenRouter¥1.75¥10.5¥0.181M65.5K

Google Vertex

Latency
1.1s
Throughput
344 tokens/s
Context
1M

Pricing

Input
¥1.75/M tokens
Output
¥10.5/M tokens
Cached
¥0.18/M tokens

Additional pricing

Input audio price
¥3.5

Specifications

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

OpenRouter

Latency
2.4s
Throughput
144 tokens/s
Context
1M

Pricing

Input
¥1.75/M tokens
Output
¥10.5/M tokens
Cached
¥0.18/M tokens

Additional pricing

Cache write
¥0.58
Cache read
¥0.18

Specifications

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

Gemini 3.1 Flash Lite Preview 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-3.1-flash-lite-preview", 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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