Google

Gemini 3.5 Flash Lite

About

Gemini 3.5 Flash Lite is a closed-weight model from Google. Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens. It accepts text, images, documents, and video. Context window is 1M. License is Gemini API Terms. Released July 21, 2026.

Context window
1M
Tokens of context on a request.
License
Gemini API Terms
Terms for the API.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.30
Cached input / 1M	$0.03
Output / 1M	$2.50

Compare

Gemini 3.5 Flash Lite is one of the catalog's chat models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M
Qwen3.5 0.8B	Multimodal chat model with up to 64 images or one video per request.	$0.08 / 1M input	0.8B	256K
Gemini 3.7 Flash	Newest Gemini Flash tier with extended reasoning. Multimodal chat + native video.	$0.75 / 1M input	—	1M
Gemini Robotics-ER 2 (preview)	Embodied-reasoning VLM for robotics. Text, multi-image, and native video.	$0.30 / 1M input	—	1M

Benchmarks

Published scores for Gemini 3.5 Flash Lite, from the Gemini 3.5 Flash-Lite model card.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	CharXiv, no tools: 74.5.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
chat	The model's reply

Estimate cost

Estimate. A page is 2,500 input tokens and 1,000 output tokens. An hour of video is the whole timeline: 258 tokens a second of picture and 32 a second of audio, plus 500 output tokens a minute.

PagesHours of video

$391.45

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="google/gemini-3.5-flash-lite",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)