Evaluation of Energy and Nutrient Estimates from Large Language Models Using Text-Based Queries

Large language models (LLMs) have emerged as promising tools for estimating energy and nutrient values, yet most existing evaluations focus on image-based queries rather than text-based queries. Few studies compare LLM estimates with reference databases commonly used in nutrition research.

This study aimed to examine agreement between LLM estimates and a research food composition database for energy and nutrients and to determine if agreement varies by food group.

We conducted a cross-sectional analysis of energy and nutrient estimates for frequently consumed food items in the United States. Food items were entered as text prompts into 4 LLMs (ChatGPT 5.2, Claude Opus 4.5, Gemini 3 Pro Preview, and Llama 4 Maverick), which provided energy and nutrient estimates. Corresponding foods were matched to the Nutrition Coordinating Center Food and Nutrient Database, and agreement between LLM and database values was assessed using intraclass correlation coefficients and Bland-Altman analyses. Agreement was also evaluated within the 3 most frequently consumed food groups.

Agreement was high for energy and macronutrients for all LLMs. Variability was observed for several micronutrients, particularly vitamin D, folate, and iron. Claude Opus 4.5 showed consistently high agreement, with no nutrients classified as poor. Other LLMs exhibited poor agreement for ≥1 micronutrient. Certain food categories, including condiments and mixed dishes, contributed disproportionately to variability. However, agreement remained high within the most frequently consumed broader food groups.

LLMs show promise for estimating energy and macronutrients. However, performance for micronutrients requires further improvement and may affect overall dietary assessment.