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Small Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial

Xiaomeng Li · 2026 · arXiv

DOI: 10.48550/arXiv.2610.26538 · open-access PDF

Portuguese

A estimativa baseada em fotos da energia alimentar poderia facilitar a avaliação dietética, e modelos multimodais de grande linguagem (LLMs) de propósito geral tornam essas estimativas fáceis de obter. A maioria das avaliações usa uma foto tirada antes de comer e compara com a energia servida, enquanto a avaliação dietética precisa de energia consumida. Avaliamos seis modelos de dois fornecedores (Claude Haiku, Sonnet e Opus 5.5; GPT-6 luna, 6.1 sol e 6 astra) em 802 refeições da ACETADA, um ensaio de alimentação controlada com ingestão ponderada, com a foto anterior apenas, antes e depois de fotos em chamadas separadas (subtração), ou ambas as fotos em uma única solicitação.

Machine translation (Meta M2M100); the English original follows.

Abstract

Photo-based estimation of food energy could ease dietary assessment, and general-purpose multimodal large language models (LLMs) make such estimates easy to obtain. Most evaluations use one photo taken before eating and compare with energy served, while dietary assessment needs energy eaten. We evaluated six models from two vendors (Claude Haiku, Sonnet and Opus 5.5; GPT-6 luna, 6.1 sol and 6 astra) on 802 meals from ACETADA, a controlled feeding trial with weighed intake, with the before photo only, before and after photos in separate calls (subtraction), or both photos in one request. Three

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