FSRS vs SM-2 is a choice between two scheduling models. SM-2 updates an ease factor and interval with a fixed formula. FSRS estimates difficulty, stability, and retrievability, then schedules toward a chosen retention target.
TL;DR
Current Anki users should generally start with FSRS. A new collection can use default parameters immediately; it does not need a fixed amount of review history first. SmartRecall currently uses SM-2 and does not provide FSRS scheduling or optimization.
How SM-2 Actually Works
SuperMemo 2 schedules cards with three numbers: easiness factor (EF), interval, and repetition count.
When you rate a card:
- Again (1): Reset interval to 1 day, reduce EF by 0.2
- Hard (2): Multiply interval by 1.2, reduce EF by 0.15
- Good (3): Multiply interval by current EF (starts at 2.5)
- Easy (4): Multiply interval by EF × 1.3, increase EF by 0.15
A card you mark "Good" three times goes: 1 day → 2.5 days → 6.25 days → 15.6 days. The EF adjusts based on your history with that specific card, but the multipliers (2.5, 1.2, 1.3) are hardcoded.
SM-2's advantage is its small, understandable state and predictable implementation. Its limitation is that it does not explicitly estimate the probability of recalling a card today.
The SM-2 Blind Spots
Three patterns SM-2 can't see:
1. Your forgetting curve isn't universal
SM-2 assumes everyone forgets at roughly the same rate. A JLPT N3 student reviewing 漢字 (kanji) and an M2 reviewing Krebs cycle intermediates have wildly different retention curves. FSRS learns your curve from your review history.
2. Card difficulty is more than one number
SM-2's easiness factor conflates "how hard is this card" with "how well do I know it right now." A card can be intrinsically difficult (complex concept) but well-learned (high stability). FSRS separates difficulty (how hard to learn initially) from stability (how long until you forget).
3. Context matters
Reviewing 50 cards in one sitting vs. 10 cards five times throughout the day produces different retention. SM-2 doesn't know. FSRS incorporates review load, time of day, and session length into its predictions.
How FSRS Schedules Differently
FSRS uses 17 parameters trained on your actual review data. The core insight: every card has two hidden properties that evolve over time.
Difficulty (D): How hard this card is to memorize, from 1 (trivial) to 10 (brutal). Set after your first few reviews, then barely changes.
Stability (S): How many days until you have a 90% chance of remembering. Increases every time you successfully recall, decreases when you forget.
When you review a card, FSRS:
- Looks at your current stability for that card
- Checks your historical accuracy at similar stability levels
- Adjusts for difficulty, recent review load, and time since last review
- Predicts four different stability outcomes (Again/Hard/Good/Easy)
- Schedules the interval that hits your target retention (default 90%)
The algorithm retrains itself every time you sync, updating those 17 parameters based on your latest reviews. After 1,000+ reviews, it knows your forgetting curve better than you do.
What public benchmarks show
The open-source SRS Benchmark compares FSRS, SM-2, and other models on large anonymized Anki datasets using prediction metrics. It can compare model calibration and discrimination, but it does not prove that every learner will reduce reviews by the same percentage or save a fixed amount of time.
When FSRS Underperforms
FSRS isn't always better. Three scenarios where SM-2 wins:
New learners with <200 reviews
FSRS needs data to calibrate. With a fresh deck, it falls back to conservative defaults that schedule more aggressively than SM-2. You'll do more reviews for the first month until the algorithm learns your curve.
Highly irregular review schedules
If you review 300 cards Monday, skip Tuesday-Thursday, then cram 400 Friday, FSRS gets confused. It assumes some consistency. SM-2's simplicity is more robust to chaos.
Shared decks across multiple people
FSRS optimizes for individual forgetting curves. If you're using a shared deck (like a med school class deck) and want everyone on the same schedule, SM-2's one-size-fits-all approach is actually a feature.
The Migration Decision Tree
Stick with SM-2 if:
- You have fewer than 1,000 mature cards (interval >21 days)
- You've been reviewing for less than 3 months
- Your review schedule is unpredictable (3+ days between sessions)
- You're using a shared deck that others depend on
Switch to FSRS if:
- You're doing 150+ reviews/day and feeling the grind
- You have 6+ months of consistent review history
- You're optimizing for time efficiency over simplicity
- You're comfortable with a 2-4 week calibration period
Try FSRS on a subset if:
- You're curious but cautious
- You have multiple decks (test it on your largest/oldest)
- You want data before committing
SmartRecall does not support FSRS or side-by-side FSRS scheduling. Use a current version of Anki or another tool that explicitly supports FSRS when FSRS is a requirement.
What the Math Actually Does
No equations, but here's the intuition:
SM-2 thinks in multipliers
"This card was easy last time, so multiply the interval by 2.5." Simple, predictable, but ignores everything except your last rating.
FSRS thinks in probabilities
"Based on 847 similar reviews, you have a 73% chance of remembering this card in 8 days and a 91% chance in 5 days. Your target is 90%, so I'll schedule it in 5.2 days." It's predicting your actual forgetting curve, not applying a formula.
The difference compounds. After 10 reviews, SM-2 might schedule a card at 180 days. FSRS might schedule it at 240 days because it knows you retain easy cards longer than the average user. That's 60 days of not reviewing something you'd remember anyway.
Retention vs Review Load Tradeoffs
Both algorithms let you tune desired retention (the percentage of cards you want to remember when they come up for review).
SM-2 at 90% retention: Predictable, but can't adapt the tradeoff per card. Your easy cards get the same retention target as your hard cards.
FSRS at 90% retention: Adapts per card. Easy cards might get scheduled at 92% retention (longer intervals), hard cards at 88% (shorter intervals), averaging to your 90% target. You spend less time on cards you'd remember anyway.
In practice, this means FSRS users can often lower their target retention from 90% to 85% without noticing a difference in exam performance, because the algorithm is better at identifying which cards actually need reinforcement.
A USMLE student I talked to runs FSRS at 87% retention and still scores 89% on UWorld blocks. "The cards I forget are the ones I'd have forgotten anyway, even with more reviews."
Implementation Notes
If you're switching:
Export your review history first
FSRS can optimize parameters from existing Anki review history. Back up the collection first and follow the current Anki and FSRS documentation.
Expect weird intervals for 2 weeks
The algorithm is calibrating. Some cards will get surprisingly long intervals, others surprisingly short. Trust the process—it's learning your curve.
Don't tweak parameters manually
FSRS has 17 parameters. Unless you have a PhD in psychometrics, let the optimizer handle it. The defaults are trained on 20+ million reviews.
Check retention after 1 month
If you're consistently below your target (e.g., 85% when you wanted 90%), the algorithm will auto-correct. If it doesn't, you might need more review history or a higher retention target.
SmartRecall has no FSRS implementation or FSRS calibration dashboard; it schedules reviews with SM-2.
The Bigger Picture
SM-2 was revolutionary in 1988 because it made spaced repetition practical. FSRS is evolutionary—it takes the same core insight (review right before you forget) and optimizes it with modern machine learning.
FSRS's value is its explicit memory model, desired-retention control, and optimizable parameters—not a universal time-saving percentage. Compare retention, workload, and consistency on the actual collection.
SmartRecall currently uses SM-2. Users who require FSRS should choose a tool that explicitly supports it; users who need editable cards generated from supported PDFs or notes can evaluate SmartRecall's authoring workflow separately.
