Gemini 2.0 Flash Thinking

Closed weights Google DeepMind,Google December 2024

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,Google
Organisation type
Industry,Industry
Country
United States of America
Published
19 December 2024
Authors
Gemini Team

What it does

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

Domain
Language, Vision, Multimodal
Task
Language modeling/generation, Quantitative reasoning, Question answering, Visual question answering, Code generation

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 Vertex AI Gemini App

How it is classified

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

Record confidence
Unknown

Sources

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

Reference
Our enhanced reasoning model, capable of showing its thoughts to improve performance and explainability
Last updated
11 February 2026

What the numbers mean

About this model

Gemini 2.0 Flash Thinking was published by Google DeepMind,Google, in United States of America, in December 2024. It comes out of industry,Industry.

It works in Language, Vision, Multimodal, and is recorded as doing language modeling/generation, Quantitative reasoning, Question answering, Visual question answering, Code generation.

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

Answers

Gemini 2.0 Flash Thinking — common questions

01

When was Gemini 2.0 Flash Thinking released?

Gemini 2.0 Flash Thinking was published in December 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

02

What is Gemini 2.0 Flash Thinking used for?

Gemini 2.0 Flash Thinking works in Language, Vision, Multimodal, and is recorded as handling language modeling/generation, Quantitative reasoning, Question answering, Visual question answering, Code generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

03

What GPU do I need to run Gemini 2.0 Flash Thinking?

None. Gemini 2.0 Flash Thinking 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.

04

Is Gemini 2.0 Flash Thinking open source?

No. Gemini 2.0 Flash Thinking has not had its weights published, so it exists only as a service controlled by its owner.

05

How many parameters does Gemini 2.0 Flash Thinking have?

No parameter count has been published for Gemini 2.0 Flash Thinking, which is why no memory or speed figure appears on this page.

06

Who created Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking was published by Google DeepMind,Google, based in United States of America, categorised as industry,Industry.

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.