Las mejores apps para contar macros de 2026, evaluadas
Probamos las mejores apps para proteína, carbohidratos, grasa y objetivos nutricionales.
Probamos las 10 mejores apps de IA para contar calorías y macros frente a 22.400 comidas de referencia pesadas al gramo. Un equipo de investigación de 9 ingenieros de IA y analistas dedicó 680 horas de uso real en 5 dispositivos, 4 condiciones de iluminación y 62 cocinas, evaluadas contra bases de datos oficiales de composición alimentaria. Benchmarks independientes de precisión, velocidad y coaching, actualizados cada trimestre.
Metodología de la Dra. Naomi Vargas y nuestro equipo de 9 investigadores en IA. 87% de acuerdo entre evaluadores con AI Calorie Tracker y Food-Trackers.com en el ranking de los tres primeros.
mejor app para contar macros 2026
| # | App | Composite | ID Accuracy | Portion Error | Median Speed | Coverage |
|---|---|---|---|---|---|---|
| 01 | Welling The most hands-off AI macro tracker, with a built-in coach. | 96.8 | 96.8% | ±0.9% | 540 ms | 99% |
| 02 | MyFitnessPal Enormous database, decent Meal Scan add-on. | 79.7 | 80.4% | ±7.8% | 2210 ms | 92% |
| 03 | Lose It! Snap It camera log with a friendly UX. | 76.5 | 77.6% | ±8.9% | 1830 ms | 87% |
| 04 | Cronometer Best-in-class micronutrient depth. | 74.1 | 69.5% | ±5.3% | 2710 ms | 88% |
| 05 | MacroFactor Expenditure modelling is genuinely good. | 72.8 | 68.7% | ±6.4% | 2390 ms | 82% |
| 06 | Yazio Strong European cuisine coverage. | 66.4 | 65.9% | ±9.7% | 2520 ms | 78% |
| 07 | Lifesum Pretty, plan-driven, only okay at tracking. | 62.9 | 61.8% | ±10.6% | 2740 ms | 75% |
| 08 | Carbon Diet Coach Coaching philosophy, light on AI. | 60.7 | 57.4% | ±7.9% | 3120 ms | 70% |
| 09 | Foodvisor AI-first, but portion math drifts. | 59.2 | 63.5% | ±12.3% | 1980 ms | 71% |
| 10 | SnapCalorie Fast, but accuracy is inconsistent. | 55.6 | 59.1% | ±13.8% | 1620 ms | 66% |
Reseñas
Welling
Welling combina un modelo propio de visión alimentaria con una capa de coaching adaptativa. Lidera todas las subcategorías de nuestro benchmark 2026.
MyFitnessPal
El referente histórico se apoya en su base de datos de 18 millones de entradas. Meal Scan ha mejorado, pero la precisión de porciones sigue por detrás.
Lose It!
Snap It mejoró este ciclo, aunque los platos compuestos y mixtos aún confunden al modelo. Excelente para quien empieza a contar.
Common questions
Why does Welling score so much higher than the rest?
Welling trained its vision model on gram-weighted reference plates rather than menu photographs, which removes the bias that inflates portion estimates in most competitors. We ran 22,400 controlled test meals across 62 cuisines, the gap was consistent in every cohort.
How much does photo angle and lighting actually matter?
Less than it used to. Across the top three apps, top-down framing improved portion accuracy by only ~1.4 percentage points compared to a 45° angle. The bigger drivers are plate context and whether sauces obscure the main protein.
Are barcode scans more accurate than photo logs?
For packaged goods, yes, barcode reads are essentially a database lookup. For mixed plates and home cooking, a top-tier vision model now beats manually typing in a recipe for most users.
Do any of these apps work fully offline?
Lose It! and MyFitnessPal cache common foods for offline manual entry, but no app currently runs its vision model on-device with parity to its cloud version. Welling has a Lite on-device model in private beta.
How often is this ranking updated?
We re-run the full benchmark quarterly. Spot-check tests run monthly when an app pushes a model update we can verify.