Vibeleaderboard
← All Intel
Intel / post

Gemini 3.1 Flash-Lite lands at $0.25 per million input tokens

Source
x.com
Date
Google AI@GoogleAI
Thread · 2 parts

Smarter. Faster. Gemini 3.1 Flash-Lite is here⚡ The model offers uncompromising speed & intelligence at scale by focusing on: — Cost-efficiency: Priced at just $0.25/1M input and $1.50/1M output tokens, it gets work done faster at a fraction of the cost of larger models, including a 45% increase in output speed compared to Gemini 2.5 Flash — Compute control: 3.1 Flash-Lite comes with flexible thinking levels so you can actively select how much the model “thinks” for a given task, based on the complexity of your project — Scalable intelligence for building: The model can handle high volume tasks at scale while keeping costs down *and* can tackle complex workloads where in-depth reasoning is needed, like generating user interfaces or detailed instruction following 3.1 Flash-Lite is rolling out in preview today via the Gemini API in @GoogleAIStudio and Vertex AI.

Video · View on X

Learn more about Gemini 3.1 Flash Lite in our blog: https://t.co/ZMo1oJAZt3

View on X

Context

Google AI released Gemini 3.1 Flash-Lite in preview through the Gemini API in AI Studio and Vertex AI. Google AI priced it at $0.25 per million input and $1.50 per million output tokens, and said it delivers a 45% increase in output speed compared with Gemini 2.5 Flash, a figure that is Google's own internal comparison rather than an independently measured .

The model ships with adjustable thinking levels, letting a developer choose how much reasoning effort it spends based on task complexity, so the same model can handle high-volume simple tasks cheaply or take on more demanding work like generating user interfaces or following detailed instructions when given more reasoning budget.

Terms in this piece · Glossary
  • token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
  • benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Read the source x.com
More from Google AI
Recommended reads
Comments

Checking sign-in…

Loading comments…