Gemini 2.5 Flash

Closed weights Google DeepMind April 2025

No estimate

No hardware requirements for this model

The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation
Google DeepMind
Organisation type
Industry
Country
United States of America
Published
17 April 2025

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Language, Multimodal, Vision, Speech, Video
Task
Language modeling/generation, Question answering, Code generation, Quantitative reasoning, Visual question answering, Translation, Image captioning, Speech recognition (ASR), Video description, Search, Text summarization, Chat

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Training data
tokens

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights
Closed — provider access only
Model access
API access
Training code
Unreleased

Availability Google AI Studio Gemini API Gemini App

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP
Yes
Record confidence
Unknown

Sources

Where this record came from and when it was last checked.

Reference
Our powerful and most efficient workhorse model designed for speed and low-cost.
Last updated
11 February 2026

What the numbers mean

About this model

Gemini 2.5 Flash was published by Google DeepMind, in United States of America, in April 2025. It comes out of industry.

It works in Language, Multimodal, Vision, Speech, Video, and is recorded as doing language modeling/generation, Question answering, Code generation, Quantitative reasoning, Visual question answering, Translation, Image captioning, Speech recognition (ASR), Video description, Search, Text summarization, Chat.

Its weights were never published, so it can only be reached through its provider. No graphics card changes that.

Answers

Gemini 2.5 Flash — common questions

01

Who created Gemini 2.5 Flash?

Gemini 2.5 Flash was published by Google DeepMind, based in United States of America, categorised as industry.

02

When was Gemini 2.5 Flash released?

Gemini 2.5 Flash was published in April 2025.

03

What is Gemini 2.5 Flash used for?

Gemini 2.5 Flash works in Language, Multimodal, Vision, Speech, Video, and is recorded as handling language modeling/generation, Question answering, Code generation, Quantitative reasoning, Visual question answering, Translation, Image captioning, Speech recognition (ASR), Video description, Search, Text summarization, Chat. These are the areas it was designed around; they describe intent rather than a hard boundary.

04

What GPU do I need to run Gemini 2.5 Flash?

None. Gemini 2.5 Flash is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.

05

Is Gemini 2.5 Flash open source?

No. Gemini 2.5 Flash has not had its weights published, so it exists only as a service controlled by its owner.

06

How many parameters does Gemini 2.5 Flash have?

No parameter count has been published for Gemini 2.5 Flash, which is why no memory or speed figure appears on this page.

Source

Original publication

Record last updated 11 February 2026

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.