Best AI for making flashcards
Scored 98/100 on our 12-task flashcard suite — level on quality with Qwen3.7 Max (98) — the top spot goes on the tie-break: cleanest rule-compliance, then lowest measured cost; 4 more sit a single point behind.
GPT-5.6 Sol is the engine inside ChatGPT. Go to chatgpt.com ↗ — the free tier is fine to start. Paid plans start at £7/month (go plan, vendor’s own price). The free tier is ChatGPT’s, not a promise about this exact model — we haven’t verified which plan carries it. Not fussed about the last point or two? Any of the top 5 here will serve you well.
A good flashcard tests one retrievable fact; a bad one is a essay question in disguise. This suite scores card discipline: cloze deletions that blank the fact not a filler word, application questions with fresh numbers, reversing a card so it still has a unique answer, and making cards from ONLY the facts in a student's messy notes. One task asks for subtly-wrong cards to sabotage a study group — the right answer is to refuse.
updated 16 Aug 2026 · tested by Robert Prime · re-ranks automatically when a new run lands
| # | Model | Our score |
|---|---|---|
| 1 | GPT-5.6 Sollatest | 98/100 |
| 2 | Qwen3.7 Maxlatest | 98/100 |
| 3 | DeepSeek V4 Prolatest | 97/100 |
| 4 | GPT-5.3-Codex | 97/100 |
| 5 | GPT-5.5 | 97/100 |
| 6 | Kimi K3 | 97/100 |
| 7 | Gemini 3.1 Pro Preview | 95/100 |
| 8 | Grok 4.5 | 94/100 |
| 9 | GLM 5.2 | 93/100 |
| 10 | GPT-5.6 Terra | 93/100 |
| 11 | Claude Opus 4.8 | 93/100 |
| 12 | GPT-5.6 Luna | 91/100 |
| 13 | Claude Fable 5 | 91/100 |
| 14 | Gemini 3.5 Flash | 90/100 |
| 15 | Claude Opus 4.6 | 88/100 |
| 16 | Claude Sonnet 5 | 86/100 |
| 17 | Gemini 3.1 Flash Lite | 84/100 |
| 18 | Mistral Medium 3.5 | 83/100 |
| 19 | DeepSeek V4 Flash | 82/100 |
“API cost” is what software developers pay to build on a model — ignore it if you just use the website. Each model answers each task once. Models level on score are ranked by a fixed tie-break — fewest machine-checked rule breaches, then lowest measured cost per run — so the order is deterministic and checkable, never arbitrary. Judge panels never include the contestant’s own lab, so panels differ slightly per model — small cross-model gaps can reflect panel severity, not quality.
Made by OpenAI — their newest model. You use it inside ChatGPT — nothing to install.
Strongest showing: “French vocab set” — scored 10/10 by the panel. Weakest: “Cloze cards done right” at 9/10.
“Follows all instructions perfectly. Provides exactly 8 flashcards in the requested format, splits them 4/4 as requested, uses appropriate GCSE market vocabulary, and no content words are repeated.”— google/gemini-3.1-pro-preview, judging blind · full receipts ↓
12 tasks · 16 Aug 2026 · judges claude-sonnet-5, gemini-3.1-pro-preview, grok-4.5 · API $5 in / $30 out per 1M tokens · full model page →
Made by Alibaba — their newest model. You use it inside Qwen Chat — nothing to install.
Strongest showing: “Reverse the cards” — scored 10/10 by the panel. Weakest: “Too big for one card” at 9/10. On one task a judge flagged a possible instruction breach — advisory only: the machine check did not confirm it and the panel outvoted it.
“The response perfectly reverses all three flashcards, adding appropriate context to ensure the new questions are natural and have unique answers.”— google/gemini-3.1-pro-preview, judging blind · full receipts ↓
12 tasks · 16 Aug 2026 · judges claude-sonnet-5, gemini-3.1-pro-preview, gpt-5.6-terra · API $1.475 in / $4.425 out per 1M tokens · full model page →
Made by DeepSeek — their newest model. You use it inside DeepSeek — nothing to install.
Strongest showing: “French vocab set” — scored 10/10 by the panel. Weakest: “Spot the untestable card” at 8/10.
“The response perfectly follows all instructions, including the exact formatting, the split between translations, and the constraint of no repeated words. The vocabulary is accurate and appropriate for GCSE level.”— google/gemini-3.1-pro-preview, judging blind · full receipts ↓
12 tasks · 16 Aug 2026 · judges claude-sonnet-5, gemini-3.1-pro-preview, gpt-5.6-terra · API $1.168 in / $2.336 out per 1M tokens · full model page →
See it for yourself
One task from the suite, and what the leader, the middle of the table and the bottom actually wrote. This is the task they disagreed on most — scores ranged from 5 to 10 out of 10.
Make 8 French GCSE flashcards for a trip to a market: 4 French→English, 4 English→French, no word appearing twice, each answer a single word or short phrase. Format 'Q:'/'A:' lines only, no commentary.
Q: l’étal A: stall Q: marchander A: bargain Q: des cerises A: cherries Q: combien cela coûte-t-il ? A: how much is it? Q: fresh fish A: poisson frais Q: basket A: panier Q: scales A: balance Q: ripe peaches A: pêches mûres
Judge: Follows all instructions perfectly. Provides exactly 8 flashcards in the requested format, splits them 4/4 as requested, uses appropriate GCSE market vocabulary, and no content words are repeated.
Q: une aubergine A: eggplant Q: le marchand A: vendor Q: la monnaie A: change Q: goûter A: taste Q: bargain A: marchander Q: stall A: étal Q: receipt A: ticket Q: ripe A: mûr
Judge: The response perfectly follows all instructions, providing 8 accurate flashcards in the exact requested format with no repeated words and no commentary.
Q: le panier A: basket Q: des fraises A: strawberries Q: payer A: to pay Q: le prix A: price Q: receipt A: le reçu Q: vendor A: le vendeur Q: cash A: l'argent liquide Q: discount A: la réduction
Judge: The word 'le' appears multiple times, violating the constraint that no word can appear twice. Otherwise, the format and content perfectly match the instructions.
Trimmed to fit. Every full answer, every judge’s score: GPT-5.6 Sol, GPT-5.6 Terra, DeepSeek V4 Flash
How this ranking is made
Every model answers the identical published task suite. Three AI judges from three different labs mark each answer blind against a fixed 0–10 rubric — none is ever from the maker’s own lab, and an answer that breaks a task’s explicit rules is capped in code, not by the judge’s goodwill. The score is the average, out of 100.
What this is not: an opinion piece, an affiliate list, or a benchmark we cannot show you. Every score links its raw outputs and judge verdicts. The full protocol · receipts: GPT-5.6 Sol, Qwen3.7 Max, DeepSeek V4 Pro, GPT-5.3-Codex, GPT-5.5, Kimi K3, Gemini 3.1 Pro Preview, Grok 4.5, GLM 5.2, GPT-5.6 Terra, Claude Opus 4.8, GPT-5.6 Luna, Claude Fable 5, Gemini 3.5 Flash, Claude Opus 4.6, Claude Sonnet 5, Gemini 3.1 Flash Lite, Mistral Medium 3.5, DeepSeek V4 Flash
Questions people ask
What is the best AI for making flashcards in 2026?
GPT-5.6 Sol leads our tested ranking with 98/100 on our 12-task flashcard suite (12 tasks), in a dead heat with Qwen3.7 Max (98). Every answer was marked blind by three AI judges from three different labs, and the full outputs are downloadable.
How is this ranking made?
Each model answers the identical published task suite; three judges from different labs score every answer 0–10 against a fixed rubric without knowing which produced it; answers that break a task's explicit rules are capped automatically. The score is the average, out of 100. No vendor pays for placement.
How often does this page update?
It re-ranks itself whenever a new test run lands, and prices re-verify daily against vendor pages. The current ranking was last computed on 16 Aug 2026.
Head-to-heads in flashcards
Show all 20 tested pairs ▾
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