# Evaluating the Energy-Efficiency of the Code Generated by LLMs

arXiv: https://arxiv.org/abs/2505.20324 · HTML: https://mlsanity.com/paper/2505.20324/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 7 of 7)

- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [EffiBench: Benchmarking the Efficiency of Automatically Generated Code](https://mlsanity.com/paper/2402.02037/) (2024)
- [On Evaluating the Efficiency of Source Code Generated by LLMs](https://mlsanity.com/paper/2404.06041/) (2024)
- [Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation](https://mlsanity.com/paper/2403.03344/) (2024)
- [Generating Energy-efficient code with LLMs](https://mlsanity.com/paper/2411.10599/) (2024)
- [An Energy-Aware Programming Approach for Mobile Application Development Guided by a Fine-Grained Energy Model](https://mlsanity.com/paper/1605.05234/) (2016)

## Built on by (most cited first, 4 of 4)

- [Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming](https://mlsanity.com/paper/2508.08332/) (2025)
- [MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models](https://mlsanity.com/paper/2606.24815/) (2026)
- [Rethinking Code Performance Benchmarks for LLMs](https://mlsanity.com/paper/2607.07619/) (2026)
- [Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting](https://mlsanity.com/paper/2610.02571/) (2026)

## Abstract (arXiv)

As the quality of code generated by Large Language Models (LLMs) improves, their adoption in the software industry for automated code generation continues to grow. Researchers primarily focus on enhancing the functional correctness of the generated code while commonly overlooking its energy efficiency and environmental impact. This paper investigates the energy efficiency of the code generated by 20 popular LLMs for 878 programming problems of varying difficulty levels and diverse algorithmic categories selected from the LeetCode platform by comparing them against canonical human-written solutions. Although LLMs can produce functionally correct results in most cases, our findings show that the performance and energy efficiency of LLM-produced solutions are often far below those of human-written solutions. Among the studied LLMs, DeepSeek-v3 and GPT-4o generate the most energy-efficient code, whereas Grok-2 and Gemini-1.5-Pro are among the least energy-efficient models. On average, human-generated canonical solutions are approximately 1.17 times more energy efficient than DeepSeek-v3, 1.21 times more energy efficient than GPT-4o, and over 2 times more energy efficient than Grok-2 and Gemini-1.5-Pro. For specific algorithmic groups such as dynamic programming, backtracking, and bit manipulation, LLM-generated code can consume up to 450 times more energy than human-generated canonical solutions.