# Google releases Gemini 3.1 Flash-Lite

_Friday, June 26, 2026 at 6:39 PM EDT · AI · Latest · Tier 2 — Notable_

![Google releases Gemini 3.1 Flash-Lite — Primary](https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gemini-3.1_flash_Lite_blog_keyword_metacard_d.width-1300.png)

Google introduced Gemini 3.1 Flash-Lite. The model is the fastest and most cost-efficient in the Gemini 3 series. It targets high-volume developer workloads at scale and delivers high quality for its price and model tier.

The model began rolling out in preview to developers via the Gemini API in Google AI Studio. Enterprises can access it through Vertex AI.

Pricing stands at 0.25 dollars per million input tokens and 1.50 dollars per million output tokens. According to the Artificial Analysis benchmark, it outperforms 2.5 Flash with 2.5 times faster time to first answer token and 45 percent higher output speed while maintaining similar or better quality. The low latency supports high-frequency workflows.

The model achieves an Elo score of 1432 on the Arena.ai Leaderboard. It records 86.9 percent on GPQA Diamond and 76.8 percent on MMMU Pro. These results surpass other models of similar tier and some larger prior Gemini models such as 2.5 Flash.

Gemini 3.1 Flash-Lite comes with thinking levels in AI Studio and Vertex AI. Developers can choose how much reasoning the model applies to a task. This feature helps manage both high-frequency workloads and more complex operations.

The model supports high-volume translation and content moderation where cost is a priority. It also handles tasks such as generating user interfaces and dashboards, creating simulations, and following instructions. It can analyze and sort large numbers of images quickly.

Early-access developers on AI Studio and Vertex AI along with companies including Latitude, Cartwheel, and Whering are already using the model. Early testers noted its efficiency and reasoning capabilities. They reported that it handles complex inputs with the precision of a larger-tier model and maintains strong instruction adherence.

## Sources

- [Google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-flash-lite/)

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