Gemini
Google's answer to GPT-4
4.8•133 reviews•5.3K followers
Google's answer to GPT-4
4.8•133 reviews•5.3K followers
Google's largest and most capable AI model. Built from the ground up to be multimodal, Gemini can generalize and seamlessly understand, operate across and combine different types of information, including text, images, audio, video and code.
This is the 12th launch from Gemini. View more
Gemini 3.1 Flash-Lite
Launched this week
Gemini 3.1 Flash-Lite is the fastest and most cost-efficient model in the Gemini 3 series. At only $0.25 input and $1.50 output per million tokens, it beats 2.5 Flash with 2.5X faster first token and 45% higher output speed while matching or beating quality.
Free Options
Launch Team
ChatGPT by OpenAI
OpenAI
GPT-5
Flowtica Scribe
Hi everyone!
I’ve been using Gemini 2.5 Flash API in my BYOK translation plugin. Switched to gemini-3.1-flash-lite-preview by literally just changing the model name — quality jumped, speed stayed the same at identical throughput, and the bill is still reasonable. Quite happy.
Official use cases like high-volume translation, content moderation, real-time image sorting, dashboard automation, UI generation and multi-step retail agents are spot on. If your app (or any slice of it) hits any of those, this one is definitely worth a shot right now in preview.
Grab it in @Google AI Studio or Vertex AI.
$0.25 input and $1.50 output per million tokens, while matching or beating 2.5 Flash quality is the number that changes the economics of high-volume AI pipelines. For anyone running thousands of generations per day, that pricing tier is the difference between a viable product margin and a problem.
The 2.5x faster first token is the other figure worth paying attention to. In real-time user-facing workflows, that latency gap is what separates an experience that feels responsive from one that feels like it's thinking.
I orchestrate multiple AI models, and the cost per generation is a constant pressure point at scale. A model at this price point that holds up on quality for tasks like content classification, asset sorting and UI generation is exactly what makes certain features economically feasible to ship. Curious how it handles multimodal consistency across a long batch run, does quality stay stable or drift?
When you're generating thousands of localized variations for Google Ads and Shopping campaigns, API costs usually eat up the margin) This price point for high-volume generation is insane. Are there any strict rate limits while it's in preview?
Multimodal was the right bet from day one, the ability to reason across text, image, and code in a single context window is something GPT-4 is still catching up on.
Using Gemini API while building Fillix - a Chrome extension that makes job hunting embarrassingly easy. The context window size alone makes it worth building on. Curious where the agentic capabilities go next.
2.5X faster first token is the real headline here. For latency-sensitive apps (chatbots, real-time assistants), that gap is massive. Curious how it handles longer context windows under load.
Awesome to see Gemini continuing to evolve! The multimodal capabilities and deep research features look really powerful. What’s the feature you think people are still sleeping on the most right now?
Super Comments
I was waiting for it, I love it, for sure I am going to add it to YouScaleIt, nice work Google!