Text / Chat

DeepSeek-V3.2 Environmental Impact

HeavyEstimated

IMO/IOI gold medalist — first model with RL-based agent training

Architecture
Mixture-of-Experts with integrated reasoning and tool use
Parameters
671B
Context
128,000 tokens
Provider
DeepSeek
5.5 Wh
Energy per query
3.0 g
CO₂ per query
20 mL
Water per query
18x more than
vs Google search

Energy per query

5.5 Wh

18x more than a Google search (0.3 Wh)

CO2 per query

3.0 g

China grid (550 gCO₂/kWh)

Water per query

20 mL

~49 queries to fill 1 litre

Processing location

DeepSeek Cloud (China) / self-hosted

Provider

DeepSeek

Category

Text / Chat

Grid carbon intensity

550 g CO2/kWh (30% renewable)

How does DeepSeek-V3.2 compare?

Ranked #118 of 152 models by energy per query

0 Wh2 Wh4 Wh6 Wh8 WhLLaMA 3.2 1BGemini 1.5 ProGPT-4.1 NanoDeepSeek-V3.2Google search (0.3 Wh)

Detailed Breakdown

Energy Consumption

DeepSeek-V3.2 integrates thinking into tool use and is the first model trained with RL across 1,800+ environments for agentic tasks. At ~5.5 Wh per query, it competes with GPT-5. The Speciale variant uses extended reasoning and won gold at IMO 2026, IOI 2026, and ICPC World Finals.

Power Source & Carbon

DeepSeek's API runs on Chinese infrastructure (550 g CO2/kWh). Open-source (MIT) and widely self-hosted globally on cleaner grids.

Water Usage

At ~20.4 mL per query on Chinese infrastructure.

About DeepSeek-V3.2

DeepSeek-V3.2 is a 671B-parameter text and chat model from DeepSeek, released December 1, 2025. IMO/IOI gold medalist — first model with RL-based agent training. At 5.5 Wh per query, it uses 18x the energy of a Google search. It runs on a Mixture-of-Experts with integrated reasoning and tool use architecture.

These figures are estimates derived from hardware specifications and API benchmarks — DeepSeek has not published official energy data for DeepSeek-V3.2. Actual consumption may vary significantly depending on batching, quantisation, and infrastructure optimisations that we cannot observe from outside.

DeepSeek-V3.2 in Context

100%
potential savings

The efficiency alternative

Gemini Nano performs the same type of task using just 0.01 Wh per query — 100% less energy than DeepSeek-V3.2. For a user sending 25 queries per day, switching would save 50.1 kWh per year.

50.2 kWh
per year

Your yearly DeepSeek-V3.2 footprint

At 25 queries per day, your annual DeepSeek-V3.2 usage consumes 50.2 kWh — a meaningful fraction of household electricity. That produces 27.6 kg of CO₂.

Key Insights

Open-source weights — can be self-hosted on infrastructure you control

What does your DeepSeek-V3.2 usage cost the planet?

Use our calculator to estimate your personal environmental footprint based on how often you use DeepSeek-V3.2.

Calculate My Compute

Frequently Asked Questions

How much energy does DeepSeek-V3.2 use per query?

Each DeepSeek-V3.2 query consumes approximately 5.5 Wh of energy. This is 18x more than a traditional Google search (~0.3 Wh).

What is DeepSeek-V3.2's carbon footprint?

Based on the carbon intensity of DeepSeek Cloud (China) / self-hosted, each query produces approximately 3.0 g of CO2. The grid in this region has a carbon intensity of 550 g CO2/kWh with 30% renewable energy.

How much water does DeepSeek-V3.2 use?

Each query consumes approximately 20 mL of water, primarily used for cooling the data centers that process the request.

How does DeepSeek-V3.2 compare to a Google search?

A DeepSeek-V3.2 query uses 18x more than a Google search in terms of energy. A Google search uses approximately 0.3 Wh, while DeepSeek-V3.2 uses 5.5 Wh.

Technical Details

Architecture

Mixture-of-Experts with integrated reasoning and tool use

Parameters

671B

Context window

128,000 tokens

Release date

2025-12-01

Open source

Yes

Training data cutoff

2025-11