It’s probably safe to say NotebookLM has been, and often still is, my go-to for a study sesh. It’s so easy yet so practical - you dump stuff in, ask questions, test your knowledge, and call it a day. The answers would point to something real instead of just sounding confident or affirming whatever’s in your prompt. Then other AI tools came into the mix, and they’ve stepped up their game in the study department.

ChatGPT got Study and Learn mode, Gemini got Guided Learning, and Claude also has its Learning Style setting. So every major AI tool is gunning for the same space NotebookLM has owned for a while. Since Claude has become my go-to bot these days, I wanted to see if it could hold its own for learning specifically. This might not be a fair fight since I did get Claude Pro recently, but that’s exactly why I put these two head-to-head, because if I’m paying for an AI, I want to know if it can pull double duty.

What I’m working with

They’re such different tools, there was no easy way to standardize this

I tried to keep this experiment as neutral as possible, given how different Claude and NotebookLM are. The goal was to use it for the same tasks - I decided to go with my learner’s license manual since I do have that exam coming up. I also had a pretty elaborate Claude memory I didn't want to lose, so at the start of every chat, I'd just prompt it to ignore all of that and work with a clean slate, similar to a notebook.

As for sources, the tools don’t accept the same inputs. NotebookLM can read weblinks and YouTube transcripts; it was designed for that. While Claude can search the web on its own terms, it can’t ingest a weblink or video the same way NotebookLM does - you can’t just hand it a URL and have it treat it as a source. So any weblinks or YouTube transcripts you work with have to be pasted into Claude as plain text - any type of reader extension will make this fast and easy. For this learner’s exam, I’m working with one long PDF document and a couple of YouTube videos.

The last big one was context. Claude and NotebookLM handle it very differently. In NotebookLM, you get a 1 million token context for analyzing large document collections at once, and clearing the chat history effectively resets the context “budget” for that session. This is why you’re able to use the same notebook over and over; the context is available to you indefinitely. In true chatbot fashion, Claude's context window runs out at some point, and you will need to start a new chat. So instead, I just created a Project to contain all my chats, and it keeps your sources and instructions loaded across every chat.

Where Claude surprised me

It’s not designed for this, but maybe I never needed NotebookLM in the first place

I’m skipping the part about NotebookLM’s performance, because we all already know what it’s capable of. I asked my questions, and it gave me cited responses. The only thing worth noting is that this time I got it to generate some quizzes and a weekend study guide to test my knowledge about the road and driving. I tried to get a similar outcome in Claude (quiz and guide), but I didn’t keep the entire process the exact same because their power does lie in their differences, and I wanted to take advantage of that.

Summarizing a document and extracting key points with Claude worked better than I expected, because it actually asked me questions back, unprompted. This back-and-forth approach is fundamentally different from NotebookLM, which just answers you; it doesn’t gauge your knowledge. The thing it was missing was proper citations - it’s worth keeping in mind that the only citation system Claude has access to is when it pulls in sources from the web. But this didn’t really matter since I primarily worked with one document.

Where it really pulled ahead of NotebookLM was in the visual department. Claude can now generate interactive visuals right in your chat - and turns out this is a game-changer for learning road signs. I was able to get it to generate custom quizzes and replicate the visuals from the PDF guide and some screenshots. The quizzes gave me a sign with four options for what the sign is, or a question with four sign options - which is the exact same format my country uses for learners’ exams. It didn’t nail every single detail of all the signs, but it was still easy to deduce which sign it was referencing. If anything, the slight inaccuracy made me work harder - I had to recall the real sign to spot the difference, which ended up engaging my memory better than a perfect replica would have.

As for the Learning Style option, it ended up not making a big difference, at least not when I got to the quiz and visual learning element. Claude was already following my instructions for the interactive diagram pretty closely and tailored its responses to my requests as a beginner driver. So if you’re relying on visuals that are driven by precise instructions, the Style probably won’t have much influence.

So which one do I actually use for studying now?

It wasn’t really a fair fight

NotebookLM does have generative visuals, but they’re not interactive like Claude’s, which means this wasn’t really a fair fight. For this task at hand in particular - an exam that involves a lot of visual elements - Claude pulled miles ahead thanks to its new interactive visual components in the chat.

However, source-grounded with citations is genuinely irreplaceable for some workflows. NotebookLM did actually pull ahead with the rules of the road module. Specific regulations, right of way rules, road markings - stuff where you want the answer traced back to the actual manual, not just a confident response.

So as for who got fired, it was NotebookLM, unfortunately. But only for this particular job. For visual interactive studying, it just can’t keep up. For anything where accuracy and traceability matter, I’ll call it back in.