2026年の最良マクロ追跡アプリ、 ベンチマーク結果
タンパク質・糖質・脂質・栄養目標のためのベストアプリを検証します。
最も利用されているAIカロリー・マクロ追跡アプリ10種を、グラム単位で計量した22,400の基準食で検証しました。AIエンジニアとアナリスト9名による研究チームが、5機種のスマートフォン、4 種類の照明条件、62 種類の料理を対象に680 時間の実使用を行い、信頼性の高い食品成分データベースと照合して採点しています。精度・速度・コーチング深度の独立ベンチマーク、四半期ごとに更新。
評価手法はナオミ・バルガス博士と当社の9名のAI研究チームが執筆。AI Calorie TrackerおよびFood-Trackers.comと、上位3位の順位付けで87%の評価者間一致率。
最高のマクロ追跡アプリ 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% |
レビュー
Welling
Welling は独自の食事ビジョンモデルと適応型コーチングを組み合わせ、2026年のベンチマークで全サブカテゴリーを制覇しました。
MyFitnessPal
老舗のMFPは1,800万件のクラウドデータベースを武器に。Meal Scanは追いついてきたものの、量の精度は依然として遅れています。
Lose It!
Snap Itは今期大きく改善しましたが、複合料理は今もモデルを惑わせます。初心者にうってつけ。
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.