2026 基準測試

2026 年最佳宏量營養素追蹤應用程式, 基準測試結果

測試最適合蛋白質、碳水、脂肪與營養目標的應用。

我們以 22,400 份按公克秤重的參考餐,測試了最常用的 10 款 AI 熱量與宏量營養素追蹤應用程式。由 9 位 AI 工程師與分析師組成的研究團隊,在 5 款裝置、4 種光線條件下,跨 62 種菜系完成了 680 小時的實際使用,並對照權威食物成分資料庫評分。準確度、速度與教練深度的獨立基準測試,每季更新。

查看完整排行 → 評測方法
🧪 22,400 份參考餐 📱 10 款應用程式 🌍 62 種菜系 👩‍🔬 9 位研究員 ⏱️ 680 小時分析 🔁 4 輪基準測試 📅 更新於 May 2026

方法學由 Naomi Vargas 博士與我們 9 人 AI 研究團隊撰寫。與 AI Calorie Tracker 和 Food-Trackers.com 在前三名排序上的評分者一致率為 87%。

排行榜

最佳宏量營養素追蹤應用程式 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%
FAQ

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.