Anki alternative:
Memia vs Anki, an honest 2026 comparison

Anki's enduring strengths

Anki's primary strength is absolute flexibility. Every aspect of the experience can be customised: card templates in HTML and CSS, add-ons for AI generation, detailed statistics, algorithm options. Advanced users get exactly the behaviour they want -- at the cost of significant initial configuration time.

The other major asset is the AnkiWeb library: thousands of medical (USMLE, ECNi), language, and academic decks built by active communities over fifteen years. For medical students in particular, the large community decks (Anking, Pepper, medical school-specific collections) are extraordinary collective resources representing hundreds of hours of communal work.

The complexity that discourages -- and its concrete consequences

But Anki was designed nearly twenty years ago for technically-minded users. Its interface reflects that legacy: dense, less intuitive, and full of settings to understand before efficient use becomes possible. Many learners quit Anki not because the method fails, but because the onboarding friction is too high.

The profiles that abandon Anki often follow similar patterns: medical students who inherit a 20,000-card deck without knowing how to configure it; language learners who spend two hours setting up HTML templates before reviewing a single card; professionals overwhelmed by advanced options. In all these cases, technical friction is the obstacle, not motivation. This is precisely the observation that drove the development of alternatives like Memia.

What FSRS models differently

FSRS models two independent dimensions for each card: memory stability (how fast recall decays for you specifically on this card) and intrinsic difficulty (how naturally hard this card is for you). It computes a review interval that maintains your retention probability at a target threshold -- 90% by default. This means FSRS does not just track whether you got a card right or wrong; it tracks how each success or failure changes your estimated forgetting trajectory for that specific item.

To illustrate the concrete difference: suppose you are reviewing a card for the third time and rate it as 'good'. With SM-2, the next interval is calculated using a fixed ease factor, identical for all users. With FSRS, the interval accounts for your complete review history on that specific card -- if you have always found it difficult, the interval will be shorter; if it is solidly anchored, it will be longer. The scheduling is personalised, not generic. Over hundreds of cards and months of review, this precision compounds into meaningfully better retention.

The measured results of FSRS vs SM-2

In practice, FSRS produces more precise long-term intervals, with fewer unnecessary reviews on mastered cards and more appropriately timed resurfacing for difficult ones. On a deck of 1,000 cards over 6 months of review, this precision significantly reduces the total number of reviews while maintaining the same retention level.

In Memia, FSRS is active by default with no configuration required. In Anki, you must find it in deck parameters, understand the configuration options, and activate it manually for each deck -- an additional barrier that leads most users to remain on SM-2 without realising there is a better option available. For a learner with 500 cards who reviews daily, the difference between SM-2 and FSRS is not dramatic. For someone with 2,000 cards over 12 months, the precision gap becomes noticeable in both retention quality and total review time.

The concrete AI generation workflow in Memia

Memia offers automatic generation: describe a topic or paste your notes, and the AI proposes structured flashcard proposals you can validate, edit, and integrate into your decks. This does not replace manual creation for key points, but it dramatically accelerates deck-building for high-volume content.

The concrete workflow: you have a chapter or document to memorise. You enter the subject and a few constraints (level, format, number of cards). In under a minute, Memia proposes a structured deck with question-answer, multiple-choice, or true/false cards suited to the content. You then review each card, rephrase those that feel imprecise, and remove redundant ones. That review process is itself a first level of active learning.

AI and deep encoding: the importance of manual validation

The validation step is learning, not administration. Reading a generated card and deciding whether it is well-formulated already mobilises your memory. Cards you rephrase in your own words anchor more deeply than those you accept passively. The ideal workflow treats AI generation as a first draft, not a finished product.

For subjects where you have no structured notes -- a self-directed topic, a new specialty, a technical book -- AI generation from a simple description saves considerable time compared to card-by-card manual creation in Anki. For subjects where you have rich existing notes, the AI reads them and generates targeted cards calibrated to what you actually need to learn.

Choose Anki if...

You are comfortable with technical configuration and want full control over every parameter. Anki rewards the time investment with exceptional precision and flexibility.

You want access to existing community decks (medicine, languages, certifications) built over years of collective effort -- especially large medical decks that represent thousands of hours of peer-reviewed card creation.

You need advanced card customisation: HTML/CSS templates, images, audio, conditional fields -- formats that Memia does not yet support at the same level.

You are on Android -- AnkiDroid is excellent and completely free, with full feature parity with the desktop version.

You want a 100% open-source tool whose longevity does not depend on any company, any funding round, or any commercial decision.

You already have years of Anki data and a large active deck in progress, and migrating would cost more than it would gain.

Choose Memia if...

You want to start reviewing immediately, without spending hours on configuration.

You want to create cards quickly from your courses, notes, or topics with AI generation.

You prefer a modern interface with a clear, intuitive progress dashboard.

You want the best spaced repetition algorithm (FSRS) without any technical effort.

You are on iOS and do not want to pay €25 for AnkiMobile when a modern alternative exists.

You are learning subjects for which no ready-made community deck exists.

Large medical decks: Anki's decisive advantage

If your learning falls into the categories covered by the AnkiWeb library -- especially health studies -- Anki has a real advantage. The large medical community decks (Anking, Pepper, Brosencephalon) represent extraordinary collective resources built over years. For students in pre-med, medical school, or preparing board exams, starting from these existing resources rather than creating everything from scratch is often the rational decision.

Memia is progressively building its deck-sharing and community catalogue features, with a catalogue of pre-built decks available for common subjects. But on this specific front, Anki retains a significant advantage that AI generation does not fully compensate -- large medical decks also contain a presentation logic and pre-tested card phrasings refined by thousands of student-hours that cannot be automatically reproduced. For medical students whose learning is inseparable from these resources, this is the most important factor in the comparison.

AI generation: the alternative for everything else

For learners whose subject is not covered by AnkiWeb -- or those who prefer cards adapted to their precise level and context -- Memia's AI generation changes the situation significantly. Rather than using a generic 5,000-word vocabulary deck created by a stranger, you create in a few minutes a deck calibrated to your actual gaps, your level, and your goals.

The quality difference is often underestimated: a card generated from your own notes mobilises associative links you already have -- whereas a generic community card requires an extra adaptation effort to integrate into your knowledge network. Custom cards built from your own material are typically faster to review and better retained.

Don't import everything: an opportunity to declutter

The most effective migration is generally not importing your entire Anki library into Memia. Card formats may differ. More importantly, it is an opportunity to declutter: after years, most users have accumulated obsolete cards, unused decks, and poorly-worded items.

Starting fresh with a prioritised subset of 200 to 500 truly essential cards, complemented by AI generation for new topics, typically produces better results than a bulk import. The quality of your deck has a direct impact on review quality -- and therefore on long-term retention. A smaller, well-curated deck beats a large, bloated one every time. This is also true within Anki itself: most experienced Anki users report that their most effective periods were with leaner, better-maintained decks, not the ones with the most cards.

The parallel transition: 4 to 8 weeks to validate

A pragmatic approach is to run both tools in parallel for 4 to 8 weeks: Anki for existing decks you do not want to abandon, Memia for all new topics. This transition period lets you evaluate concretely whether Memia meets your needs before switching entirely.

During this period, observe two things: the quality of Memia reviews (do the intervals feel well-calibrated to your actual forgetting rate?) and the relative friction of both tools day-to-day. If after 6 weeks Memia has integrated naturally into your routine and Anki feels heavy to maintain, the decision to switch becomes straightforward. If instead you find yourself missing Anki's customisation or community decks, you have useful data: keep Anki for those specific use cases and use Memia as a complement for new content.