ChatGPT vs Gemini for Making Quizzes: Which One Handles Your Document Better

For making a quiz from a document, Gemini has the clearer document ceiling and ChatGPT has the more flexible output. Google publishes hard numbers for Gemini document processing: 50 MB and 1,000 pages per file, at roughly 258 tokens a page. ChatGPT is easier to push into a specific output shape on request. Neither one hands you a numbered student copy with a separate answer key, and that gap, not question quality, is what decides which tool a teacher or trainer actually keeps using.

This comparison assumes the real job: you have a chapter, a manual, or a deck, and you need questions that cover it. Not a topic you want the model to invent questions about from memory. That distinction changes which weaknesses matter.

The short comparison

Which one reads a long PDF better?

Gemini, on paper, because Google states the limit instead of leaving you to discover it. A 1,000 page allowance covers essentially every course reading and most training manuals. The catch is that being accepted and being covered are different things. At 258 tokens a page, a 300 page manual is around 77,000 tokens of source material before your prompt, and question coverage across that much text drifts toward whatever the model weighted heaviest.

ChatGPT behaves similarly on long files, retrieving passages rather than reading the document evenly end to end. The practical result is the same on both: ask for 30 questions from a long document and you will get 30 good looking questions drawn unevenly from it. The fix is also the same, and it is worth doing before you trust any generated set. Ask the model to list every major section and state how many questions came from each. Gaps show up in seconds.

Which writes better questions?

Close enough that prompt quality matters more than model choice. Both write clean stems. Both default to definition recall when you do not tell them otherwise. Both write weak distractors, which is the single most common quality problem in AI generated multiple choice, and the one that most often lets a student pick the right answer without understanding the material.

The wording that moves quality on either model is specific: ban phrase matching, require a short scenario in each stem, and require every wrong option to reflect a real misunderstanding rather than an obviously absurd choice. Keeping the options the same length removes the classic giveaway, where the correct answer is the longest one because it carries the caveats. There is more on that failure mode in can AI write good multiple choice questions.

Which handles scanned documents better?

Both process pages visually and both degrade with the scan, not with the model. Google's documentation notes that pages are rescaled during processing, with larger pages reduced to a maximum of 3072 by 3072 pixels. That rescale is why small print in a low quality scan turns into wrong numbers and mangled terms in the questions. A flat, well lit scan at a normal resolution works on either tool. A skewed phone photo of a textbook spread does not, on either.

If most of your source material is scanned, that is worth planning around rather than discovering item by item. The diagnostics are in how to tell if a PDF is scanned or searchable.

Where both of them stop

This is the part that decides the workflow, and it is identical on both tools. You end up with questions and answers interleaved in one block of chat text. Turning that into something usable means four manual steps every single time:

• Split the answers out into a separate document, then keep the numbering aligned after you cut two questions you did not like.
• Clean up stray formatting so it prints as a test rather than as a chat transcript.
• Add answer space, a header, and point values if it is going on paper.
• Retype everything into Forms, Canvas, or a quiz platform if anyone other than you is taking it.

Once a week, across a semester, that is the bulk of the time. The generation step was never the expensive part. This is also where the two tools converge into the same answer: neither is a quiz builder, and neither claims to be. A purpose built generator takes the same upload and returns the numbered student copy and the matching answer key as two separate files, which removes all four steps at once.

So which should you use?

Use Gemini when the source lives in Google Drive and the document is large, because the limits are published and the account is already there. Use ChatGPT when you need the output in a very specific shape and you are willing to spell it out, because it holds a detailed format instruction well. Use a dedicated generator when the quiz has to become a document somebody else uses, which is most of the time in a classroom or a training team.

The prompt libraries for each are on the ChatGPT quiz generator and Gemini quiz generator pages, along with the honest comparison against uploading the file directly.

One workflow note that applies whichever you pick. The source document you are quizzing from is usually the same document you need in two or three other shapes that week: a slide deck for the session, a summary for the email, a handout. Teams that handle that well tend to run one source through a content repurposing workflow rather than rebuilding each version by hand, and the quiz is simply one more output of the same document.

Frequently asked questions

Is Gemini better than ChatGPT for making quizzes?

Not outright. Gemini has published document limits of 50 MB and 1,000 pages and sits inside Google Workspace, which wins when your material is already in Drive. ChatGPT follows detailed output instructions more reliably. Question quality is close enough on both that your prompt matters more than your choice of model.

How many pages can each one read?

Google documents 1,000 pages and 50 MB per document for Gemini, and rejects files past either limit rather than truncating them. OpenAI does not publish an equivalent simple page ceiling for chat uploads, so the practical test with ChatGPT is to upload the file and then ask which sections it can see.

Can either one give me an answer key as a separate file?

No. Both return the questions and answers in the same reply. You can ask for the key at the end, which helps, but splitting it into a real second document and keeping the numbering aligned after edits is manual work on both.

Do I still have to check the questions?

Yes, on both. Read each item against the passage it came from, and pay particular attention to numbers, dates, and distractors. Five minutes of review catches nearly everything, and skipping it is how a question with two defensible answers reaches a class.

Which is better for making a quiz in Google Forms?

Neither solves the real bottleneck. Google Forms has no bulk import for a block of text, so questions get added one at a time or through an add on script regardless of which model wrote them. That constraint is covered in detail on PDF to Google Form.