TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation

While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of execution efficiency remains overlooked. We present trace, the first benchmark to explicitly assess efficiency in LLM-translated code. trace includes 1,000 efficiency-critical tasks across C++, Java, and Python, each augmented with stress tests that reveal efficiency degradations often overlooked by small-scale tests. Using trace, we conduct an extensive evaluation of 28 representative LLMs and highlight several key insights: 1) Correctness is not a reliable proxy for efficiency: the correctness leader Claude-4-think achieves only mid-level time efficiency, outperformed by smaller open-source LLMs such as Qwen2.5-Coder-14B-Instruct. 2) Inefficiency is both prevalent and patterned: 23.5% of correct translations exhibit pronounced inefficiency, distributed across algorithmic faults (11.9%), language construct mismatches (66.4%), and resource mismanagement (21.7%). 3) Inference-time prompt strategies bring only modest improvements, suggesting that current LLMs lack intrinsic efficiency awareness. Together, our results establish efficiency as an essential dimension of code translation and position trace as a principled foundation for efficiency-oriented evaluation.