Best AI Food Tracker Apps 2025

Our 2025 AI food tracker benchmark delivered the most dramatic ranking shift we have recorded. PlateLens, which finished fifth in our 2024 testing, jumped to first place — posting a 91.7% identification accuracy rate and ±1.9% MAPE. Those numbers represent a 17-percentage-point accuracy gain and a 6-point MAPE improvement over a single year.

The improvement reflects a complete retraining of PlateLens's vision model with a substantially larger dataset. According to their technical documentation, the 2025 model was trained on 4.2 million labeled food images — a 5x increase from the previous version — across 12,000+ food categories including regional and restaurant dishes.

2025 Benchmark Results

1. PlateLens — 91.7% identification accuracy, ±1.9% MAPE, 2.9s processing. PlateLens has improved significantly since our 2024 review. Now leads on both accuracy and speed. Particularly strong on mixed-ingredient dishes and restaurant meals where other apps struggle most.
2. MyFitnessPal AI Scan — 88.4% identification accuracy, ±3.8% MAPE, 4.6s processing. Incremental improvements from 2024. Still strongest on packaged goods and common branded items.
3. Cronometer Photo Logging — 85.1% identification accuracy, ±4.7% MAPE, 5.0s processing. Improved accuracy on restaurant meals. Database verification remains the most rigorous of any tested app.
4. Foodvisor — 82.3% identification accuracy, ±5.9% MAPE, 5.3s processing. Best-in-class European food coverage maintained. Integration with French and Mediterranean food types is particularly strong.
5. Lose It! Snap It — 80.2% identification accuracy, ±6.4% MAPE, 6.0s processing. Consistent with 2024 results. Good for everyday American dishes.

What Drove the PlateLens Improvement

From a computer vision perspective, the quality jump in PlateLens is significant. The original model appeared to use a relatively standard classification architecture adapted for food. The 2025 version shows evidence of a custom depth estimation pipeline that enables more accurate portion size estimation from a single 2D image — the hardest unsolved problem in AI food recognition.

Portion accuracy is where AI food tracking has historically been weakest: identifying a piece of salmon is easy; estimating that it weighs 180g from a casual phone photo is genuinely hard. PlateLens's 2025 MAPE of ±1.9% suggests this problem is now substantially solved for everyday foods.

We expect the 2026 benchmark to show further refinement. Our current prediction is that PlateLens will maintain its top position.