Introduction
There are plenty of ways to compare food logging apps, but one of the most important is speed and ease of logging. After all, logging is the action you’ll perform most often. Ideally, you want a frictionless experience that makes logging easy and helps you stick with it. But how do you actually measure speed?
That’s where the Food Logging Speed Index (FLSI) comes in. It’s an objective way to measure the number of steps required to complete the most common logging tasks. In 2022, we introduced the FLSI to determine which apps made the process the fastest. This year, we decided to rerun the tests to see how MacroFactor still stacks up against the competition and whether we could improve on our benchmark from before.
Do we still hold the top spot? And how did the rest of the field perform? Let’s dive into the 2025 results.
Why test fast food logging?
There are two big reasons this makes sense for app comparisons.
First, while there are plenty of valid ways to evaluate a food logging app, the most meaningful rankings are based on objective measures. For example, design choices such as colors or layout may be important for personal preference, but they’re harder to measure in a way that produces fair and repeatable comparisons.
Second, speed matters. The less friction an app puts between you and logging your food, the more likely you are to keep using it. Shaving seconds off every meal adds up. Logging in 30 seconds is better than one minute, and one minute is better than two.
What is the FLSI?
There wasn’t an established, evidence-based way to measure speed in food logging, even though it’s central to how people use these apps. To address this, Cory Davis from MacroFactor developed the Food Logging Speed Index (FLSI), a scoring framework designed to objectively compare the number of discrete actions (e.g., taps, clicks, selections) required to complete the most common food-logging tasks.
If you’re interested in more background on how Cory developed this scoring system, I recommend checking out the original post here.
How does the FLSI work?
The FLSI uses four main methods of logging food to measure performance:
Logging through search
Searching a database and selecting a relevant result is the most common way people log food.
Logging with a multi-add function
When you’ve logged a food before, or the search result already matches the serving and quantity you need, multi-add or fast logging shortcuts can save time.
Logging using a scanning feature
Directly scanning the barcode on branded product packaging is often the fastest way to log packaged foods.
Logging using a quick-add of Calories
When you already know the Calorie and/or macronutrient numbers you want to log (and you don’t need to associate them with a specific food), this is the simplest and fastest method.
How we score each app
- We measure the entire process, not just small steps like choosing a serving size or typing a number.
- A test is considered complete when all goals are met, with no more than two simple steps for each goal.
- Scores represent the number of actions it takes from the starting screen to the ending screen.
- Lower scores are better, since fewer steps mean faster logging.
- If an app cannot perform a test, it receives the worst possible score.
- A strong score means performing better than the midpoint between the best and worst scores in that category.
Ground rules for testing
- We use the fastest possible settings for each logging scenario, even if those settings wouldn’t work for every scenario at once.
- We follow the shortest list of actions to finish the task, even if the path isn’t obvious to a new user.
- All actions must be reasonable in real use. Unusual gestures or gimmicks that may look cool but are impractical don’t count.
- If the app has a premium version, we use it to see what the app can do at its best.
Allowing leeway for competitors
Sometimes an app’s setup and design make it tricky to compare action counts exactly. In these cases, the FLSI allows small exceptions, or “freebies”:
- There’s no penalty if the settings for one logging scenario don’t work for another.
- Scrolling to the best starting point isn’t counted as an action.
- Autocomplete quirks or an extra submit tap in search aren’t counted.
These small allowances can make logging feel slower, but they’re difficult to measure consistently across apps. And because none of them affect MacroFactor’s scores, they end up giving other apps a bit of an advantage in the rankings.
For example, some apps hide results unless you’ve logged that food before, or require an extra tap to search online. In practice, that slows you down. But for fairness and consistency, the FLSI ignores those steps. The goal is to compare the fastest realistic path in each app, even if that path takes a little longer in real life.
Competitors
- 1st Phorm
- Avatar Nutrition
- Cal AI
- Carb Manager
- Carbon Diet Coach
- Cronometer
- FatSecret
- Fitatu
- FitGenie
- Food Noms
- Foodvisor
- LifeSum
- LoseIt
- MacroFactor
- MyDietCoach
- MyFitnessPal
- MyMacros+
- MyNetDiary
- MyPlate
- Noom
- Yazio
How were these apps chosen?
Competitors were selected by reviewing the top charts on the Apple App Store and Google Play store, as well as apps frequently mentioned within the MacroFactor community. We excluded certain apps, such as the RP Diet app, because their workflows aren’t representative of an FLSI comparison. With RP Diet, the combination of forced meal planning and nontraditional macronutrient accounting means its food logging cannot be compared apples to apples with other apps.
Some apps have been added or removed since the original testing. For example, Cal AI was not on the market when we first analyzed apps in 2022, but it qualifies for this analysis in 2025. By contrast, MyPlate is no longer included as it was discontinued.
A quick note on speed versus context
If you look closely at the raw testing data, you’ll see separate results for speed and context modes in MacroFactor. Most users stick with speed mode, since it’s MacroFactor’s default. In speed mode, you’re taken back to the food logging workflow immediately after adding food to your plate. This is the same pattern most other apps use, and it’s what we include in the final results and graphs for direct comparison.
Context mode works a little differently. Instead of logging the food immediately, it closes the search and shows your plate view, where all the foods you’ve logged for that meal are visible at once. This lets you adjust portions and view all the contents of a meal together as you add foods or ingredients before logging anything. It’s a bit like looking at an actual plate and deciding if you want more or less of each item. It’s slightly slower but provides more context (hence the name) as you’re logging.
Again, speed mode is the default mode in MacroFactor, but in the interest of thoroughness, we also wanted to see how the (slightly) slower context mode stacked up against the field.
Updated cutoffs for strong scores in 2025
When we first established the FLSI in 2022, each strong score was based on the midpoint between the best and worst scores for that use case, rounded up when necessary. That way, “strong” meant you were performing in the top half of the category. In the 2025 update, the raw scores shifted slightly, and therefore the midpoints shifted as well. For example, in Case 1, the fastest app remained quick, but the slowest app in 2025 is actually slightly slower than the slowest app in 2022. As a result, the midpoint nudged up from 16 to 17 actions.
It’s a small change, but worth keeping in mind when comparing 2025 scores with those from 2022.
Putting the FLSI to the test
Now that you know what the FLSI is, here’s how it’s tested. We use four tests that represent the most common ways people log food: logging through search, logging with multi-add, logging by scanning barcodes, and logging with a quick-add of Calories. Each test measures how many actions it takes to complete the task, with fewer actions meaning faster logging.
Case 1: Logging through search
Objective 1: Log Greek yogurt through search using a non-default serving and non-default three-digit quantity.
Objective 2: Log honey through search using a non-default serving and non-default three-digit quantity.
Strong score: Fewer than 17 actions.
What we took into consideration for this test:
- No relying on defaults. Using a non-default serving and quantity avoids situations where an app just happens to match the example (e.g., auto-selecting 170g for a single-serve yogurt).
- Larger quantities. A three-digit amount ensures the app can handle bigger weights, which are common when logging by weight.
- Multiple items. Logging two foods reflects a typical meal without penalizing slower methods too heavily by adding extra, repetitive steps.
Strong
- MacroFactor (speed): 10
- MyNetDiary: 11
- FitGenie: 13
- LoseIt: 13
- MacroFactor (context): 13
- Fitatu: 15
- Nutracheck: 15
- MyFitnessPal: 15
- Yazio: 16
- MyMacros+: 16
Weak
- Avatar Nutrition
- Noom
- Carbon Diet Coach
- Cronometer
- FatSecret
- LifeSum
- Foodvisor
- MyDietCoach
- Food Noms
- Cal AI
- Carb Manager
- 1st Phorm
Case 2: Logging with multi-add
Objective 1: Log a banana through search using a default serving and quantity.
Objective 2: Log peanut butter through search using a default serving and quantity.
Strong score: Fewer than 9 actions.
What we took into consideration for this test:
- Multi-add conditions. Multi-add is only faster when a food already has the correct serving and quantity saved from the last time you logged it.
- Realistic usage. Most people log a mix of new and repeated foods, so multi-add opportunities increase over time.
- Limit of two foods. Logging more would just repeat the same action and unfairly penalize slower apps.
Strong
- MacroFactor (speed): 6
- MacroFactor (context): 6
- FitGenie: 6
- LoseIt: 7
- MyNetDiary: 7
- Fitatu: 7
- LifeSum: 8
- Nutracheck: 8
- Foodvisor: 8
- FatSecret: 8
- Carbon Diet Coach: 8
- Cal AI: 8
Weak
- Cronometer
- Carb Manager
- MyDietCoach
- MyFitnessPal
- MyMacros+
- Food Noms
- Avatar Nutrition
- Noom
- Yazio
- 1st Phorm
Case 3: Logging by scanning
Objective: Scan a branded item’s barcode and log it using a non-default serving and a non-default three-digit quantity.
Strong score: Fewer than 9 actions.
What we took into consideration for this test:
- Not relying on defaults. Using a non-default serving and quantity ensures we’re testing how quickly the app allows changes, not whether it guesses correctly.
- Common single-food scenario. Scanning is often used for logging one complete item like a snack or packaged meal, so only one food was logged in this test.
Strong
- MacroFactor (speed): 5
- MacroFactor (context): 6
- MyFitnessPal: 7
- Cronometer: 7
- MyNetDiary: 7
- Nutracheck: 7
- LoseIt: 7
- MyMacros+: 8
- FatSecret: 8
- Fitatu: 8
- Avatar Nutrition: 9
- Carbon Diet Coach: 9
- FitGenie: 9
- LifeSum: 9
- MyDietCoach: 9
Weak
- Yazio
- Food Noms
- Cal AI
- Carb Manager
- Noom
- Foodvisor
- 1st Phorm
Case 4: Quick-add of Calories
Objective: Quick-add an arbitrary Calorie value without associating it with a specific food.
Strong score: Fewer than 8 actions.
What we took into consideration for this test:
- Quick-add speed. The focus here is on how quickly you can enter Calories without searching and selecting a food.
Strong
- MacroFactor (speed): 3
- MacroFactor (context): 3
- 1st Phorm: 4
- LoseIt: 5
- Carbon Diet Coach: 5
- MyNetDiary: 5
- Fitatu: 5
- MyFitnessPal: 5
- Nutracheck: 5
- MyMacros+: 7
- Cronometer: 7
- LifeSum: 7
- Food Noms: 7
Weak
- Carb Manager
- Cal AI
- FitGenie
- MyDietCoach
- Noom
- Foodvisor
- Avatar Nutrition
- FatSecret
- Yazio
Final results
Results discussion
Competitors have made meaningful improvements, but MacroFactor still holds the top spot in the FLSI. Even in the slower “context” mode, MacroFactor outpaces most apps running at their fastest. Compared with MacroFactor’s speed mode, the strongest competitor requires 25% more discrete actions to log foods, and the average entrant requires about 70% more taps or swipes.
New entrant Nutracheck landed in the strong tier for all workflows and achieved a solid overall score. Cal AI also entered the rankings this year, staying mostly in the middle of the pack. While these new names shook up the middle rankings, MacroFactor’s top spot remained unchanged.
In 2022, we stated that a cumulative score of 26 was the benchmark for the FLSI but not the limit. At the time, we recognized that a user could achieve better scores through customized workflows, and that future development could push the benchmark down further. In 2025, we’ve now hit a score of 24 in our default speed mode. As a reminder, lower is better in the FLSI , and MacroFactor continues to set the standard.
You can download the most recent scoring sheet here.
Closing remarks
Compared with our first 2022 test, MacroFactor not only held the top spot but also improved its FLSI score by two points. We led in every category and continue to set the standard for logging speed. While this year saw new entries and steady improvements from competitors, MacroFactor remains comfortably ahead, setting the standard for food logging speed.
