Les meilleures applis de suivi des macros de 2026, évaluées
Nous testons les meilleures applis pour la protéine, les glucides, les lipides et les objectifs nutritionnels.
Nous avons testé les 10 applis d'IA de suivi des calories et des macros les plus utilisées sur 22 400 repas de référence pesés au gramme. Une équipe de 9 ingénieurs IA et analystes a mené 680 heures d'utilisation réelle sur 5 appareils, 4 conditions d'éclairage et 62 cuisines, comparées à des bases de données nutritionnelles officielles. Benchmarks indépendants de précision, vitesse et coaching, mis à jour chaque trimestre.
Méthodologie signée par la Dre Naomi Vargas et notre équipe de 9 chercheurs IA. 87% d'accord inter-évaluateurs avec AI Calorie Tracker et Food-Trackers.com sur le classement des trois premières applis.
meilleure appli de suivi des 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% |
Avis
Welling
Welling associe un modèle propriétaire de vision alimentaire à une couche de coaching adaptative. Elle domine toutes les sous-catégories de notre benchmark 2026.
MyFitnessPal
Le pionnier s'appuie sur sa base de 18 millions d'entrées. Meal Scan progresse, mais la précision des portions reste en retrait.
Lose It!
Snap It a progressé ce cycle, mais les plats composés et mixtes piègent encore le modèle. Excellent pour les débutants.
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.