Which AI Models Use the Most Energy?

# CLEER Dashboard | AI Inference Energy+CO2e Source: [https://cleer.pages.dev/](https://cleer.pages.dev/) This dashboard applies**CLEER**, a new approach for estimating the environmental impact of closed AI models\. It combines peer\-reviewed research, large\-scale empirical testing, and practical application to corporate emissions inventories\. **2,400\+**test runs **425 million\+**tokens **Largest to date**production\-representative open\-model energy test #### How CLEER works **CLEER**\(Closed\-model Latent Energy Estimation Range\) estimates per\-token energy for models that cannot be measured directly\. It benchmarks open models on known hardware, matches proprietary models to the closest proxies based on observed performance, and projects their energy use onto measured power curves\.[Technical Report →](https://reports.sustainableaigroup.com/CLEER-Tech-Report) #### What the comparisons show The dashboard translates per\-token estimates into representative**chat**and**agentic**sessions derived from public production datasets\. Every model receives the same token workload, so differences primarily reflect energy intensity rather than how verbose each model tends to be\. Actual results will also depend on the number of tokens a model generates in practice\. Workload sources: ShareChat conversations, Qwen serving traces, and AgentX coding\-agent sessions\. #### From energy to emissions Results include accelerator and host\-server energy, idle capacity, cooling and power distribution, and embodied emissions from hardware and data center construction\.**Scenario default:**behind\-the\-meter gas generation at**640 gCO₂e/kWh**, combined with US\-average data center assumptions for PUE \(1\.45\)\. Both values can be toggled in the dashboard\. Learn more:[Emissions Calculation →](https://cleer.pages.dev/#)