FSRS vs SM-2: Which Spaced Repetition Algorithm Wins in 2026?

I’ll say the biased part up front: I built Flashcard, a spaced-repetition tool, so I have a stake in this conversation. But the FSRS vs SM-2 question is one I get asked honestly enough, by people comparing algorithms before they’ve even picked an app, that it’s worth answering straight — including the parts where the answer isn’t “use my tool.”

If you’ve spent any time in r/Anki or the Anki Forums, you’ve seen the debate: SM-2 is the classic scheduler nearly every spaced-repetition app has run for decades, and FSRS is the newer algorithm that’s been steadily replacing it, including as Anki’s own default since version 23.10 in late 2023. Here’s the plain comparison, the honest verdict, and when each one actually wins.

FSRS vs SM-2 at a glance

	SM-2	FSRS
Year / origin	1987, SuperMemo (Piotr Woźniak)	2022, open-source community project, now Anki’s default (v23.10+)
How it models memory	One “ease factor” per card, adjusted up or down after each review	Three separate variables per card: difficulty, stability, and retrievability
Review efficiency	Adequate, but tends to over-schedule reviews to hit a target retention	Schedules meaningfully fewer reviews at the same retention target, per Anki developer benchmarks and community testing
Data needed to work well	Works reasonably from the first review	Improves as it accumulates review history; needs a real usage history to fit parameters well
Best for	Small decks, short-term cramming, apps without FSRS support	Long-term study, large decks, anyone doing more than roughly 1,000 reviews

What is SM-2?

SM-2 is the scheduling algorithm SuperMemo introduced in 1987, and it’s the one nearly every spaced-repetition app you’ve used — Anki for most of its life included — was built on. It works by giving every card a single “ease factor” that goes up when you recall the card easily and down when you struggle, and it uses that one number to decide how much to stretch the interval before the next review.

That simplicity is SM-2’s strength and its limit at the same time. Because it collapses “how hard is this card” and “how well do I currently remember it” into one adjustable number, it can’t distinguish a card that’s inherently difficult from one you just haven’t reviewed in a while. The practical effect is that SM-2 tends to be a little conservative — it shows you cards more often than strictly necessary to hit your retention target, because it doesn’t have the resolution to know it could safely wait longer.

SM-2’s other defining trait is that it’s stateless in a useful way: it doesn’t need a large history of your review data to start making reasonable scheduling decisions. A brand-new deck with a handful of reviews logged behaves about as sensibly under SM-2 as one with years of history. That’s part of why it spread to so many flashcard apps beyond SuperMemo itself — it’s simple to implement, predictable to reason about, and doesn’t require any machine-learning-style fitting step to work. For nearly three decades, “spaced repetition” and “SM-2” were more or less synonymous in the flashcard-app world.

What is FSRS?

FSRS (Free Spaced Repetition Scheduler) is a modern, open-source scheduling algorithm that models each card’s difficulty, stability, and retrievability as three separate values instead of one blended ease factor. Difficulty captures how inherently hard the card is for you; stability captures how long you can currently go between reviews before forgetting; retrievability is the live estimate of your recall probability at any given moment.

Because those three things are tracked independently, FSRS can be more precise about when a card actually needs to resurface. A card that’s easy but hasn’t been reviewed in a while gets treated differently than a card that’s genuinely hard, even if both currently sit at similar recall probability — a distinction SM-2 can’t make. This is the reason FSRS schedules fewer reviews for the same retention target: it’s not being more aggressive, it’s being more accurate about which cards are safe to leave alone longer.

The tradeoff is that FSRS needs data to be good at this. Its parameters are fit from your own review history — how often you’ve gotten a card right, how long you waited between reviews, how that outcome shifted over time — and with too little history, its estimates of your personal difficulty and stability per card are little better than guesses. This is exactly why Anki, when it made FSRS the default scheduler starting in version 23.10 (released late 2023), kept the option to switch back to SM-2 in deck options: the migration only pays off once you’ve logged enough reviews for FSRS to actually know something about how you forget things.

So which one actually wins?

FSRS is the better default for most learners in 2026. It schedules roughly 20-30% fewer reviews than SM-2 at an equivalent retention rate, a figure that comes from Anki developer benchmarking and community testing rather than a hard scientific constant, but it’s been reproduced enough in the Anki community to trust directionally. Fewer reviews for the same retention means less daily review time for the same long-term recall — which is the entire point of a scheduling algorithm.

That said, SM-2 is genuinely fine in a couple of specific situations:

• You’re under roughly 1,000 total reviews. FSRS fits its per-card parameters from your review history, so with a thin history it has little room to outperform SM-2. Below that rough threshold, the algorithm choice barely matters.
• You’re cramming for something short-term, like an exam in two weeks, where long-run scheduling efficiency doesn’t matter because you’re not trying to retain the material for years.
• Your current tool doesn’t support FSRS and switching tools just to get a marginally better scheduler isn’t worth the disruption to a habit that’s already working.

If none of those apply to you — you’re a long-term learner with a real review history and a tool that supports it — FSRS is the more efficient choice, and there’s no real downside to switching.

It’s also worth being honest about what “20-30% fewer reviews” doesn’t mean. It doesn’t mean you’ll learn 20-30% faster in some absolute sense, and it isn’t a guarantee that applies uniformly to every deck or every learner — it’s a benchmark result, reproduced across enough community testing to be a reasonable expectation, not a law of memory. What it reliably does mean is a lighter daily review queue for the same long-run retention, which for most people translates into a study session that takes less time and feels less like a chore. That’s a real, meaningful gain — just not a magic one.

What retention target should I set?

A requested retention of 0.85 to 0.90 (85-90%) is a sensible starting point for most learners using FSRS. Retention here means the probability the algorithm is aiming for that you’ll recall a given card correctly when it resurfaces — set it too high (say, 0.95) and you’ll get a heavier daily review queue in exchange for forgetting cards less often; set it too low and you save time but blank on cards more frequently. Most people are better served nudging it down toward 0.85 if their queue feels unsustainable rather than chasing a retention number for its own sake — a schedule you actually keep up with beats a theoretically optimal one you abandon after two weeks.

A few practical notes on choosing a number:

• If you’re studying for a long-term goal — a language, a professional certification you’ll use for years — a lower retention target like 0.85 is usually the better tradeoff, since you’ll review the material again regardless and a slightly higher forget rate now costs you little.
• If you’re cramming for a near-term deadline, a higher target like 0.92-0.95 makes more sense, since you need the material solid on a specific date and won’t get many more passes at it before then.
• If your daily review count keeps creeping up and it’s making you dread opening the app, that’s a signal to lower your retention target, not a signal to switch tools. The queue size is a direct, adjustable consequence of the number you picked.

There’s no universally “correct” retention value — it’s a genuine tradeoff between review time and forgetting rate, and the right setting depends on what you’re actually optimizing for.

The part where I’m honest about my own bias

Here’s where the algorithm question actually connects to the tool question, and where I want to be careful not to overclaim. Flashcard doesn’t advertise FSRS. It uses spaced repetition scheduling, full stop — I’m not going to tell you it runs a specific named algorithm it doesn’t, because that’s exactly the kind of claim this article is arguing against making carelessly.

What I do believe, and what years of watching people (including myself) actually study has shown me, is that the algorithm matters less than whether you open the app. A theoretically superior scheduler used twice a week loses to a merely adequate one you open every day, because the compounding advantage of daily consistency dwarfs a 20-30% efficiency gain you only capture sporadically. That’s the design bet behind Flashcard: make decks fast enough to build and review that opening the app daily is the path of least resistance, rather than trying to win the algorithm debate outright.

If you’re deciding between apps on the basis of which scheduler they run, that’s a reasonable thing to research — this article exists because it’s a legitimate question. But if you’re deciding between “the app with the better algorithm that I’ll open twice a month” and “the app I’ll actually open every day,” pick the second one, whatever algorithm sits under the hood.

Related reading

If you want the fuller mechanics of how spaced-repetition scheduling works in general, I go through it in more plain-language detail in how spaced repetition scheduling works. If you’re studying Chinese specifically and want to see this applied to a real workflow, spaced repetition for Chinese SRS covers that ground. And if you’re weighing Flashcard against Anki directly rather than just the algorithms underneath them, Flashcard vs Anki for Chinese vocabulary is the honest side-by-side.

The other question that tends to come right after “which algorithm is better” is “how many cards should I actually be reviewing per day” — that’s a separate, equally important variable, and I cover it in how many flashcards to study per day.

You can try Flashcard’s spaced-repetition decks at flashcard.fulinlabs.com — no algorithm marketing, just a tool built to be fast enough that you’ll actually use it tomorrow.