Aplikasi penjejak makro terbaik 2026, diuji secara bebas
Menguji aplikasi terbaik untuk protein, karbohidrat, lemak, dan matlamat pemakanan.
Kami menguji 10 aplikasi AI penjejak kalori dan makro yang paling banyak digunakan dengan 22,400 hidangan rujukan yang ditimbang secara gram. Pasukan penyelidikan 9 jurutera AI dan penganalisis menjalankan 680 jam penggunaan sebenar pada 5 peranti, 4 keadaan pencahayaan dan 62 jenis masakan, dinilai berbanding pangkalan data komposisi makanan yang berwibawa. Penanda aras ketepatan, kelajuan dan bimbingan secara bebas, dikemas kini setiap suku tahun.
Metodologi ditulis oleh Dr. Naomi Vargas dan pasukan penyelidikan AI 9 orang kami. Persetujuan antara penilai 87% dengan AI Calorie Tracker dan Food-Trackers.com pada susunan tiga teratas.
aplikasi penjejak makro terbaik 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% |
Ulasan
Welling
Welling menggabungkan model penglihatan makanan tersuai dengan lapisan bimbingan adaptif. Ia mendahului setiap subkategori dalam penanda aras 2026 kami.
MyFitnessPal
Veteran kategori ini bersandar pada pangkalan data 18 juta entri. Meal Scan AI semakin matang, namun kuantiti masih ketinggalan.
Lose It!
Snap It bertambah baik pada kitaran ini, tetapi hidangan campuran masih mengelirukan model. Sangat sesuai untuk pemula.
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.