The increasing adoption of Large Language Models (LLMs) as a text analysis method in social science presents a critical yet under-examined trade-off between model performance and environmental sustainability. This research provides a systematic evaluation comparing the performance, energy consumption, processing time, and CO 2 emissions of various computational text analysis methods (CTAM), including dictionaries, trained classifiers, and self-hosted open LLMs when performing sentiment analysis of parliamentary speeches, classification of open-ended survey responses, and named entity recognition of newspapers. The analysis is limited to self-hosted deployment in local and server environments where per-task energy consumption is directly measurable. Although self-hosted LLMs demonstrate strong performance in sentiment analysis, closely aligning with human judgment, they require significantly more energy and time than non-LLM approaches. For classification and named entity recognition, pretrained task-specific models achieve better F1 scores with a lower carbon footprint, challenging the primacy of larger models. To navigate this trade-off, we propose a CO 2 -Adjusted F1 Score that penalizes emissions while rewarding performance. Applying this metric, we show that smaller, task-specific models may be preferred over larger general-purpose LLMs for efficient text analysis. We highlight the necessity for thoughtful and responsible model selection, promoting a “right-fit” approach for CTAM.