Calculate Your Daily Anki Cards
See step-by-step calculation
This calculator determines how many new Anki flashcards you should study each day and how many daily review sessions to expect, based on your total deck size and target completion window. It uses the core math behind the SM-2 spaced repetition algorithm: New Cards/Day = Total Cards ÷ (Weeks × 7). As new cards mature, each one generates a compounding trail of scheduled reviews — on average, a single learned card produces roughly 8–12 reviews over a 30-day window at default Anki settings. Understanding this multiplication effect is critical before committing to a large deck, because review debt can accumulate faster than most learners anticipate.
This calculator determines how many new Anki flashcards you should study each day and how many daily review sessions to expect, based on your total deck size and target completion window. It uses the core math behind the SM-2 spaced repetition algorithm: New Cards/Day = Total Cards ÷ (Weeks × 7).
When to use this calculator
- Medical student planning to memorize 3,000+ anatomy terms before Step 1 USMLE without burning out from review overload
- Language learner building a 2,000-word Japanese vocabulary deck for the JLPT N3 exam in 16 weeks
- Bar exam candidate scheduling 1,500 legal definition cards across 10 weeks while balancing other study obligations
- Pre-med student estimating whether a 500-card biochemistry deck is feasible alongside a full course load before finals
Example calculation
- 1000 cards, 12 weeks
- ~12 per day
How it works
3 min readHow It's Calculated
The two core outputs are derived as follows:
# New cards per day
new_per_day = total_cards / (weeks * 7)
# Peak daily reviews (SM-2 default multiplier ≈ 10×)
max_reviews_per_day = new_per_day * 10
# Example: 1,000 cards, 12 weeks
new_per_day = 1000 / (12 * 7) = 1000 / 84 ≈ 11.9 → ~12 cards/day
max_reviews = 12 * 10 = ~120 reviews/dayThe 10× multiplier is an empirically derived estimate from Anki's default SM-2 settings: each new card, once learned, schedules reviews at intervals of ~1 day, 3 days, 7 days, 14 days, and so on. Within a rolling 30-day window at steady state, a card introduced 30 days ago will have been reviewed approximately 5–7 times; cards across all maturity stages in the deck collectively average out to ~10 review events per new card introduced per day. Advanced users with longer intervals (ease factor > 2.5) can drop this to ~6–7×; beginners pressing "Again" frequently may see 12–15×.
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Reference Table
| Deck Size | Weeks | New/Day | Peak Reviews/Day | Est. Daily Study Time* |
|---|---|---|---|---|
| 500 | 8 | ~9 | ~90 | ~30 min |
| 500 | 12 | ~6 | ~60 | ~20 min |
| 1,000 | 10 | ~14 | ~140 | ~45 min |
| 1,000 | 12 | ~12 | ~120 | ~40 min |
| 1,000 | 20 | ~7 | ~70 | ~23 min |
| 2,000 | 16 | ~18 | ~180 | ~60 min |
| 3,000 | 24 | ~18 | ~180 | ~60 min |
| 3,000 | 36 | ~12 | ~120 | ~40 min |
\*Estimated at ~20 seconds per review card (Anki community benchmark).
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Typical Use Cases with Real Numbers
Case 1 — USMLE Step 1 (Anking deck, ~30,000 cards):
Most students don't start from scratch; they work a pre-made deck. A student starting 6 months (≈26 weeks) before the exam who targets 8,000 active cards would set ~44 new cards/day, producing ~440 reviews/day — roughly 2.5 hours of Anki alone. Most experienced Step 1 studiers cap new cards at 50/day and accept a longer finish line.
Case 2 — JLPT N3 vocabulary (2,000 words, 16 weeks):
new_per_day = 2000 / 112 ≈ 18 cards/day. Peak reviews = ~180/day ≈ 60 minutes. This is aggressive but achievable if the learner already knows hiragana/katakana and can leverage mnemonics to keep the "Again" rate below 15%.
Case 3 — Light casual use (500 cards, 12 weeks):
new_per_day = 500 / 84 ≈ 6 cards/day. Peak reviews ≈ 60/day ≈ 20 minutes. This is the sweet spot for most hobbyists and professionals fitting Anki into a lunch break.
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Common Mistakes
1. Ignoring the review multiplier. Learners see "12 new cards/day" and think that's their total daily workload. In reality, the review queue is 8–12× larger at steady state. Skipping reviews for even 3–4 days creates a backlog that can feel impossible to clear.
2. Setting new cards too high at the start. Introducing 30+ new cards/day before your ease factors stabilize causes "ease hell" — cards get marked "Again" repeatedly, compressing intervals and flooding the review queue with immature cards.
3. Not accounting for weekends or zero-review days. Anki's algorithm schedules cards for a specific calendar date. Missing a day doesn't make the card disappear — it reschedules for the next session, doubling that day's load. A 5-day-a-week plan effectively means ~1.4× the daily card count on study days.
4. Confusing deck size with learnable cards. A 3,000-card deck often contains 400–600 cards that are too complex to learn standalone (e.g., multi-concept cloze deletions). Effective deck size for planning is typically 80–85% of the raw card count.
5. Underestimating early interval failures. During the first 2–3 weeks, new learners typically fail 25–40% of new cards (pressing "Again"), which short-circuits the interval and re-queues those cards the same or next day — effectively multiplying the early load beyond the 10× steady-state estimate.
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Frequently asked questions
What does the 10× review multiplier actually mean in practice?
When you introduce 12 new cards per day using Anki's default SM-2 settings, those cards don't disappear after you first learn them — they return for review at increasing intervals (1, 3, 7, 14, 30+ days). At steady state (typically reached after 3–4 weeks of consistent study), the cumulative daily review queue averages about 10 reviews for every 1 new card introduced per day. So 12 new cards/day → ~120 reviews/day. This ratio drops to ~6–7× for experienced users with high ease factors and rises to 12–15× for beginners with high failure rates.
How long does each Anki review session realistically take?
The most commonly cited benchmark in the Anki community is approximately 15–25 seconds per card, with 20 seconds being the standard estimate for mixed decks. At 120 reviews/day × 20 seconds = 40 minutes. Pure recognition cards (image occlusion, basic Q&A) average ~12–15 sec; complex cloze deletions or cards requiring active recall of multi-step processes can take 30–45 sec each. Plan your schedule using 20 sec/card for initial estimates, then adjust after your first two weeks of data.
Is there a science-backed optimal number of new cards per day?
There is no single universally validated number, but cognitive science literature on the spacing effect (Ebbinghaus, 1885; Cepeda et al., 2006 in Psychological Bulletin) consistently shows that spreading learning over time dramatically outperforms massed practice. Practical consensus in the Anki community and memory research suggests 10–20 new cards/day is sustainable for most learners, with diminishing retention above 30/day due to working memory saturation and increased failure rates. Medical students using the Anking deck often target 20–50/day during dedicated study periods but report significant fatigue above 50.
What happens if I miss a day of reviews?
Anki does not delete or reset missed cards — it reschedules them all for your next login, creating a backlog spike. Missing 1 day on a 120-review/day deck means facing ~240 reviews the next session. Missing 3 days can produce 300–400+ reviews in a single session. The SM-2 algorithm does apply a small interval penalty for overdue cards, but the psychological burden of a large backlog is the bigger risk. Setting a 'maximum reviews per day' cap in Anki settings (Deck Options → Reviews → Maximum reviews/day) prevents overwhelming sessions but delays card maturation.
How does this calculator's formula relate to Anki's official SM-2 algorithm?
The SM-2 algorithm, developed by Piotr Wozniak at SuperMemo and used as the basis for Anki's scheduler, determines review intervals using an ease factor (default 2.5) and an interval multiplier. The formula for the next interval is: I(n) = I(n-1) × EF, where EF starts at 2.5 and adjusts based on your recall grade (0–5). This calculator simplifies the forward-planning problem: given a fixed deck size and deadline, it outputs the sustainable daily new-card rate and the expected review load using the empirically derived steady-state ratio, without requiring you to know your personal EF in advance.
Should I use a pre-made deck or make my own cards?
Research on the generation effect (Slamecka & Graf, 1978) suggests self-generated material is recalled better than passively received content, because the act of writing a card encodes it more deeply. However, making cards is time-intensive (~2–5 min per card). For standardized exams (USMLE, MCAT, bar exam, language certifications), high-quality pre-made decks like Anking (medicine) or Core 2k/6k (Japanese) offer a practical trade-off: you sacrifice some generation benefit but gain hundreds of hours of card-creation time. Most expert Anki users recommend using a pre-made base deck and modifying or adding cards rather than building from scratch.
What's the maximum deck size a typical person can realistically learn?
There is no hard biological ceiling, but maintenance becomes the bottleneck. A fully mature 10,000-card deck at default Anki settings requires approximately 50–80 reviews/day just for maintenance (roughly 17–27 minutes). World-class memory athletes and polyglots (e.g., users documented on r/medicalschoolanki) report maintaining 30,000–50,000-card decks, but that typically requires 2–4 hours of daily Anki. For most learners with limited time, a working vocabulary of 3,000–5,000 well-chosen cards strikes the best balance between coverage and time investment.
How do I calculate how long it takes to finish a deck if I cap my daily reviews?
If you set a review cap of, say, 150/day and have 1,500 cards at a 10× review ratio, you can safely introduce 15 new cards/day without exceeding your cap. Completion time = Total Cards ÷ New Cards/Day = 1,500 ÷ 15 = 100 days ≈ 14.3 weeks. However, in the first 2–3 weeks your actual review count will be well below your cap (since cards haven't matured yet), meaning you can safely push new cards slightly higher initially and taper as the queue grows. This 'front-loading' strategy is common among exam-focused students.