Gemini 3.1 Flash-Lite  |  Gemini API  |  Google AI for Developers
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The Interactions API is now generally available. We recommend using this API for access to all the latest features and models.
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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.
Try in Google AI Studio
gemini-3.1-flash-lite
Property
Description
id_cardModel code
gemini-3.1-flash-lite
saveSupported data types
Inputs
Text, Image, Video, Audio, and PDF
Output
Text
token_autoToken limits[*]
Input token limit
1,048,576
Output token limit
65,536
handymanCapabilities
Audio generation
Not supported
Caching
Supported
Code execution
Supported
Computer use
Not supported
File search
Supported
Function calling
Supported
Grounding with Google Maps
Supported
Image generation
Not supported
Live API
Not supported
Search grounding
Supported
Structured outputs
Supported
Thinking
Supported
URL context
Supported
speedConsumption options
Batch API
Supported
Flex inference
Supported
Priority inference
Supported
123Versions
Read the model version patterns for more details.
Stable: gemini-3.1-flash-lite
calendar_monthLatest update
May 2026
cognition_2Knowledge cutoff
January 2025
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:
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:
from google import genai
client = genai.Client()
# 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",
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:
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:
from google import genai
from google.genai import types
import httpx
client = genai.Client()
# 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",
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 uses Flash-Lite to
classify task complexity and route to Flash or Pro accordingly.
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:
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)
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-06-23 UTC.
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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-06-23 UTC."],[],[]]
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