> ## AI Agent Context
> Get the complete documentation index at: https://aistudio.google.com/llms.txt
> Use this file to discover all available pages.
> For the full documentation in a single file, fetch: https://aistudio.google.com/llms-full.txt

# Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite is a low-latency, cost-effective multimodal model
optimized for high-frequency, lightweight tasks. The model supports text, image,
video, audio, and PDF inputs, and is designed for high-volume agentic workflows,
simple data extraction, and applications where latency and API cost are the
primary constraints.

{% button href="https://aistudio.google.com/prompts/new_chat?model=gemini-3.1-flash-lite" variant="ais" %}Try in Google AI Studio{% /button %}

## gemini-3.1-flash-lite  {% id="gemini-31-flash-lite" %}

{% table variant="model" %}
  * Property
  * Description

  ---

  *     {% section class="model-property" %}
  {% icon name="id_card" /%}Model code

    {% /section %}

  * `gemini-3.1-flash-lite`

  ---

  *     {% section class="model-property" %}
  {% icon name="save" /%}Supported data types

    {% /section %}

  *     **Inputs** Text, Image, Video, Audio, and PDF

    **Output** Text


  ---

  *     {% section class="model-property" %}
  {% icon name="token_auto" /%}Token limits{% sup %}[[*]](/docs/tokens){% /sup %}

    {% /section %}

  *     **Input token limit** 1,048,576

    **Output token limit** 65,536


  ---

  *     {% section class="model-property" %}
  {% icon name="handyman" /%}Capabilities

    {% /section %}

  *     {% section class="three-column-grid" %}
  {% section %}
  **Audio generation**

  {% badge type="not-supported" /%}

  {% /section %}

  {% section %}
  **Batch API**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Caching**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Code execution**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Computer use**

  {% badge type="not-supported" /%}

  {% /section %}

  {% section %}
  **File search**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Flex inference**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Function calling**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Grounding with Google Maps**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Image generation**

  {% badge type="not-supported" /%}

  {% /section %}

  {% section %}
  **Live API**

  {% badge type="not-supported" /%}

  {% /section %}

  {% section %}
  **Priority inference**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Search grounding**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Structured outputs**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **Thinking**

  {% badge type="supported" /%}

  {% /section %}

  {% section %}
  **URL context**

  {% badge type="supported" /%}

  {% /section %}

    {% /section %}


  ---

  *     {% section class="model-property" %}
  {% icon name="123" /%}Versions

    {% /section %}

  *     Read the [model version patterns](/docs/models#model-versions) for more details.

    *   `Stable: gemini-3.1-flash-lite`


  ---

  *     {% section class="model-property" %}
  {% icon name="calendar_month" /%}Latest update

    {% /section %}

  * May 2026

  ---

  *     {% section class="model-property" %}
  {% icon name="id_card" /%}Model card

    {% /section %}

  * [Model card](https://deepmind.google/models/model-cards/gemini-3-1-flash-lite/)

  ---

  *     {% section class="model-property" %}
  {% icon name="paid" /%}Pricing

    {% /section %}

  * [Pricing](/docs/pricing#gemini-3.1-flash-lite)
{% /table %}

## Developer guide  {% id="developer-guide" %}

Gemini 3.1 Flash-Lite is best at handling straightforward tasks at significant
scale. Here are some use cases best suited for Gemini 3.1 Flash-Lite:

*   **Translation**: Fast, cheap, high-volume translation, such as processing
  chat messages, reviews, and support tickets at scale. You can use system
  instructions to constrain output to only the translated text with no extra
  commentary:

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

*   **Transcription**: Process recordings, voice notes, or any audio content
  where you need a text transcript without spinning up a separate
  speech-to-text pipeline. Supports multimodal inputs, so you can pass audio
  files directly for transcription:

  ```python
  from google import genai
  
  client = genai.Client()
  
  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",
    contents=[prompt, uploaded_file]
  )
  
  print(response.text)
  ```

*   **Lightweight agentic tasks and data extraction**: Entity extraction,
  classification, and lightweight data processing pipelines supported with
  structured JSON output. For example, extracting structured data from an
  e-commerce customer review:

  ```python
  from google import genai
  from pydantic import BaseModel, Field
  
  client = genai.Client()
  
  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",
      contents=[prompt, input_text],
      config={
          "response_mime_type": "application/json",
          "response_json_schema": ReviewAnalysis.model_json_schema(),
      },
  )
  
  print(response.text)
  ```

*   **Document processing and summarization**: Parse PDFs and return concise
  summaries, like for building a document processing pipeline or quickly
  triaging incoming files:

  ```python
  from google import genai
  from google.genai import types
  import httpx
  
  client = genai.Client()
  
  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",
      contents=[
          types.Part.from_bytes(
              data=doc_data,
              mime_type='application/pdf',
          ),
          prompt
      ]
  )
  
  print(response.text)
  ```

*   **Model routing**: Use a low-latency and low-cost model as a classifier that
  routes queries to the appropriate model based on task complexity. This is a
  real pattern in production - the open-source [Gemini CLI](https://geminicli.com/docs/core/#model-fallback) uses Flash-Lite to
  classify task complexity and route to Flash or Pro accordingly.

  ```python
  from google import genai
  
  client = genai.Client()
  
  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",
      contents=user_input,
      config={
          "system_instruction": CLASSIFIER_SYSTEM_PROMPT,
          "response_mime_type": "application/json",
          "response_json_schema": response_schema
      },
  )
  
  print(response.text)
  ```

*   **Thinking**: For better accuracy for tasks that benefit from step-by-step
  reasoning, configure thinking so the model spends additional compute on
  internal reasoning before producing the final output:

  ```python
  from google import genai
  from google.genai import types
  
  client = genai.Client()
  
  response = client.models.generate_content(
      model="gemini-3.1-flash-lite",
      contents="How does AI work?",
      config=types.GenerateContentConfig(
          thinking_config=types.ThinkingConfig(thinking_level="high")
      ),
  )
  
  print(response.text)
  ```