Text / Chat

Gemini 1.5 Flash Environmental Impact

Ultra-efficientEstimated

Google's fastest and most efficient production model

Architecture
Multimodal Transformer (Mixture-of-Experts, distilled)
Context
1,000,000 tokens
Provider
Google
0.10 Wh
Energy per query
0.01 g
CO₂ per query
0.11 mL
Water per query
about the same as
vs Google search

Energy per query

0.10 Wh

about the same as a Google search (0.3 Wh)

CO2 per query

0.01 g

Google Global Network grid (300 gCO₂/kWh)

Water per query

0.11 mL

~9,091 queries to fill 1 litre

Processing location

Google global network (64% renewable)

Provider

Google

Category

Text / Chat

Grid carbon intensity

300 g CO2/kWh (64% renewable)

How does Gemini 1.5 Flash compare?

Ranked #6 of 152 models by energy per query

0 Wh0.07 Wh0.14 Wh0.21 Wh0.28 WhGemini 1.5 FlashLLaMA 3.2 1BGemini 1.5 ProGPT-4.1 Nano

Detailed Breakdown

Energy Consumption

Gemini 1.5 Flash is one of the most energy-efficient major models at approximately 0.10 Wh per query. Distilled from the larger Gemini 1.5 Pro, it was designed for high-volume, latency-sensitive applications. It runs on Google's custom TPU chips, which are purpose-built for transformer inference. Flash is one of the highest-volume models globally due to its speed and low cost.

Power Source & Carbon

Runs on Google's global TPU infrastructure with 100% renewable energy matching and a PUE of 1.10. The efficiency of Flash makes it particularly well-suited for green inference at scale.

Water Usage

At approximately 0.11 mL per query, Gemini 1.5 Flash has one of the lowest water footprints of any major model — benefitting from both its computational efficiency and Google's cooling infrastructure.

About Gemini 1.5 Flash

Gemini 1.5 Flash is a text and chat model from Google, released in May 24, 2024. Google's fastest and most efficient production model. Each query uses 0.10 Wh of energy and produces 0.01 g of CO₂. That's roughly comparable to a traditional Google search. It ranks in the top quartile of text and chat models for energy efficiency (#6 of 94).

Gemini 1.5 Flash benefits from running in Google Global Network, one of the cleaner grid regions in our dataset at 300 gCO₂/kWh with 64% renewable energy. The same model running in a coal-heavy region would produce significantly more carbon per query.

These figures are estimates derived from hardware specifications and API benchmarks — Google has not published official energy data for Gemini 1.5 Flash. Actual consumption may vary significantly depending on batching, quantisation, and infrastructure optimisations that we cannot observe from outside.

Gemini 1.5 Flash in Context

0.9 kWh
per year

Your yearly Gemini 1.5 Flash footprint

At 25 queries per day, your annual Gemini 1.5 Flash usage consumes 0.9 kWh — less than charging your phone for a year. That produces 0.1 kg of CO₂.

Key Insights

Top 10 most energy-efficient model across 152 models tracked
Uses less than a third of the average energy for text and chat models

What does your Gemini 1.5 Flash usage cost the planet?

Use our calculator to estimate your personal environmental footprint based on how often you use Gemini 1.5 Flash.

Calculate My Compute

Frequently Asked Questions

How much energy does Gemini 1.5 Flash use per query?

Each Gemini 1.5 Flash query consumes approximately 0.10 Wh of energy. This is about the same as a traditional Google search (~0.3 Wh).

What is Gemini 1.5 Flash's carbon footprint?

Based on the carbon intensity of Google global network (64% renewable), each query produces approximately 0.01 g of CO2. The grid in this region has a carbon intensity of 300 g CO2/kWh with 64% renewable energy.

How much water does Gemini 1.5 Flash use?

Each query consumes approximately 0.11 mL of water, primarily used for cooling the data centers that process the request.

How does Gemini 1.5 Flash compare to a Google search?

A Gemini 1.5 Flash query uses about the same as a Google search in terms of energy. A Google search uses approximately 0.3 Wh, while Gemini 1.5 Flash uses 0.10 Wh.

Technical Details

Architecture

Multimodal Transformer (Mixture-of-Experts, distilled)

Context window

1,000,000 tokens

Release date

2024-05-24

Open source

No

Training data cutoff

2024-11