Google

Gemini 2.5 Flash Lite

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

Gemini 2.5 Flash-Lite 是Gemini 2.5 系列中的一个轻量级推理模型,优化了超低延迟和成本效率。与早期的 Flash 模型相比,它提供了更好的吞吐量、更快的 token 生成以及在常见基准测试中更好的性能。可通过reasoning.max_tokens开启思考并控制思维链长度。

Input / output modalities
文本 · 图像 to 文本
Reference input / output price
Input¥0.7Output¥2.8per 1M tokens
Context window
1M
Added to catalog
Jun 24, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Google Vertex¥0.7¥2.8¥0.181M64K
OpenRouter¥0.7¥2.8¥0.071M64K

Google Vertex

Latency
2.26s
Throughput
337 tokens/s
Context
1M

Pricing

Input
¥0.7/M tokens
Output
¥2.8/M tokens
Cached
¥0.18/M tokens

Specifications

Context
1M
Max output
64K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

OpenRouter

Latency
3.65s
Throughput
2140 tokens/s
Context
1M

Pricing

Input
¥0.7/M tokens
Output
¥2.8/M tokens
Cached
¥0.07/M tokens

Additional pricing

Cache write
¥0.58
Cache read
¥0.07

Specifications

Context
1M
Max output
64K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Gemini 2.5 Flash Lite 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-flash-lite", 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}")
                
              

Application data is temporarily unavailable. Model details remain usable.

Related Models