Is it accurate?
Here's the honest answer.
We benchmark Luno against USDA references and real meals every month. This page shows exactly how it performs — including where it still has work to do.
of meals land within ±10% of their true calorie count.
Median calorie error across the full sample is 7%. Below, the per-macro and per-category breakdown — including the dishes where we miss.
Where the numbers land
Each macro is measured separately because precision differs. Fat is hardest because the same dish carries a wide grams-per-portion spread; calories are easiest because errors in macros partially cancel.
Where we excel — and where we don't
An average accuracy number across all meals hides where the model is strong and where it isn't. Here's the breakdown by what kind of food we're looking at.
How we measure
A short, honest walk-through of how the numbers above were produced.
USDA reference comparison
Every meal is scored against USDA FoodData Central where available. For dishes outside the database, we use hand-labelled ground truth from a registered dietitian.
Real-world sampling
We don't grade ourselves on photos from a lab. The benchmark draws a stratified random sample from production logs across cuisines, meal types, and times of day.
Continuous re-validation
The benchmark re-runs weekly against the latest model. Numbers on this page reflect the most recent full run; the date above tells you when.
What we're great at — and where we're not
We'd rather tell you up front than have you discover it logging your fourth meal.
- Packaged foods with visible nutrition labels.
- Common single-protein dishes (grilled chicken, salmon, eggs).
- Staple grains and legumes with standard portions.
- Drinks with well-known nutrition profiles.
- Mixed bowls with multiple unknown ingredient portions.
- Restaurant dishes outside chain menus we've ingested.
- Novel regional cuisines outside our curated reference set.
- Hand-prepared homemade meals where portion guesses dominate the error.
The feedback loop
Every correction a user makes feeds back into the reference data. The next person logging that dish gets a more accurate read.
Don't take our word for it.
Log a meal in Luno. Compare it to the label, or to your usual tracker. If we're off, tell us — every correction makes the next read better.