Gemini 3.1 Flash-Lite 预览版

我们最具成本效益的多模态模型，可为高频轻量级任务提供最快的性能。Gemini 3.1 Flash-Lite 最适合处理海量代理任务、简单的数据提取任务，以及预算和速度是主要限制因素的极低延迟应用。

gemini-3.1-flash-lite-preview

开发者指南

Gemini 3.1 Flash-Lite 最擅长大规模处理简单任务。以下是一些最适合使用 Gemini 3.1 Flash-Lite 的应用场景：

• 翻译：快速、经济实惠的大规模翻译，例如大规模处理聊天消息、评价和支持请求。您可以使用系统指令将输出限制为仅包含翻译后的文本，而不包含额外的注释：

  text = "Hey, are you down to grab some pizza later? I'm starving!"
  
  response = client.models.generate_content(
      model="gemini-3.1-flash-lite-preview",
      config={
          "system_instruction": "Only output the translated text"
      },
      contents=f"Translate the following text to German: {text}"
  )
  
  print(response.text)
  

• 转写：处理录音、语音记事或任何需要文本转写的音频内容，而无需启动单独的语音转文字流水线。支持多模态输入，因此您可以直接传递音频文件进行转写：

  # URL = "https://storage.googleapis.com/generativeai-downloads/data/State_of_the_Union_Address_30_January_1961.mp3"
  
  # Upload the audio file to the GenAI File API
  uploaded_file = client.files.upload(file='sample.mp3')
  
  prompt = 'Generate a transcript of the audio.'
  
  response = client.models.generate_content(
    model="gemini-3.1-flash-lite-preview",
    contents=[prompt, uploaded_file]
  )
  
  print(response.text)
  

• 轻量级智能体任务和数据提取：支持实体提取、分类和轻量级数据处理流水线，并以结构化 JSON 格式输出。例如，从电子商务客户评价中提取结构化数据：

  from pydantic import BaseModel, Field
  
  prompt = "Analyze the user review and determine the aspect, sentiment score, summary quote, and return risk"
  input_text = "The boots look amazing and the leather is high quality, but they run way too small. I'm sending them back."
  
  class ReviewAnalysis(BaseModel):
      aspect: str = Field(description="The feature mentioned (e.g., Price, Comfort, Style, Shipping)")
      summary_quote: str = Field(description="The specific phrase from the review about this aspect")
      sentiment_score: int = Field(description="1 to 5 (1=worst, 5=best)")
      is_return_risk: bool = Field(description="True if the user mentions returning the item")
  
  response = client.models.generate_content(
      model="gemini-3.1-flash-lite-preview",
      contents=[prompt, input_text],
      config={
          "response_mime_type": "application/json",
          "response_json_schema": ReviewAnalysis.model_json_schema(),
      },
  )
  
  print(response.text)
  

• 文档处理和总结：解析 PDF 并返回精炼的摘要，例如用于构建文档处理流水线或快速分诊传入的文件：

  import httpx
  
  # Download a sample PDF document
  doc_url = "https://storage.googleapis.com/generativeai-downloads/data/med_gemini.pdf"
  doc_data = httpx.get(doc_url).content
  
  prompt = "Summarize this document"
  response = client.models.generate_content(
      model="gemini-3.1-flash-lite-preview",
      contents=[
          types.Part.from_bytes(
              data=doc_data,
              mime_type='application/pdf',
          ),
          prompt
      ]
  )
  
  print(response.text)
  

• 模型路由：使用低延迟且低成本的模型作为分类器，根据任务复杂程度将查询路由到适当的模型。这是生产环境中的一种实际模式 - 开源 Gemini CLI 使用 Flash-Lite 对任务复杂程度进行分类，并相应地将任务路由到 Flash 或 Pro。

  FLASH_MODEL = 'flash'
  PRO_MODEL = 'pro'
  
  CLASSIFIER_SYSTEM_PROMPT = f"""
  You are a specialized Task Routing AI. Your sole function is to analyze the user's request and classify its complexity. Choose between `{FLASH_MODEL}` (SIMPLE) or `{PRO_MODEL}` (COMPLEX).
  1.  `{FLASH_MODEL}`: A fast, efficient model for simple, well-defined tasks.
  2.  `{PRO_MODEL}`: A powerful, advanced model for complex, open-ended, or multi-step tasks.
  
  A task is COMPLEX if it meets ONE OR MORE of the following criteria:
  1.  High Operational Complexity (Est. 4+ Steps/Tool Calls)
  2.  Strategic Planning and Conceptual Design
  3.  High Ambiguity or Large Scope
  4.  Deep Debugging and Root Cause Analysis
  
  A task is SIMPLE if it is highly specific, bounded, and has Low Operational Complexity (Est. 1-3 tool calls).
  """
  
  user_input = "I'm getting an error 'Cannot read property 'map' of undefined' when I click the save button. Can you fix it?"
  
  response_schema = {
    "type": "object",
    "properties": {
      "reasoning": {
        "type": "string",
        "description": "A brief, step-by-step explanation for the model choice, referencing the rubric."
      },
      "model_choice": {
        "type": "string",
        "enum": [FLASH_MODEL, PRO_MODEL]
      }
    },
    "required": ["reasoning", "model_choice"]
  }
  
  response = client.models.generate_content(
      model="gemini-3.1-flash-lite-preview",
      contents=user_input,
      config={
          "system_instruction": CLASSIFIER_SYSTEM_PROMPT,
          "response_mime_type": "application/json",
          "response_json_schema": response_schema
      },
  )
  
  print(response.text)
  

• 思考：对于需要逐步推理的任务，为了提高准确性，请配置思考，以便模型在生成最终输出之前花费额外的计算资源进行内部推理：

  response = client.models.generate_content(
      model="gemini-3.1-flash-lite-preview",
      contents="How does AI work?",
      config=types.GenerateContentConfig(
          thinking_config=types.ThinkingConfig(thinking_level="high")
      ),
  )
  
  print(response.text)