NotebookLM adds full 1 million‑token Gemini context window, boosts processing
When you’re trying to pull insights from a thick report or a sprawling code repo, hitting the limit of what the model can remember feels like a roadblock. NotebookLM markets itself as a focused partner for digging into dense material, but until now it could only hold a modest slice of text in one go. That means if you’re juggling a multi-page study or a long literature review, the chat window cuts off, and you end up re-typing or breaking the analysis into separate prompts.
I’ve seen that happen a lot - the friction is real, especially when the chat is the main way to query large document sets. The latest update seems to change the game by unlocking Gemini’s 1 million-token window for all plans. In theory, that should let the tool keep far more content in view, so you don’t lose the thread while sifting through big archives.
It looks like a direct answer to the bottleneck that’s been slowing down deeper, continuous exploration.
We have significantly expanded NotebookLM's processing capabilities, conversation context and history. Starting today, we're enabling the full 1 million token context window of Gemini in NotebookLM chat across all plans, significantly improving our performance when analyzing large document collections. Plus, we've increased our capacity for multiturn conversation more than sixfold, so you can get more coherent and relevant results over extended interactions.
We have enhanced how NotebookLM finds information in your sources. To help you uncover new connections, it now automatically explores your sources from multiple angles, going beyond your initial prompt to synthesize findings into a single, more nuanced response.
A million-token window sounds like a big jump, but it might just be a modest tweak for most of us. NotebookLM now opens up Gemini’s full 1 million-token context on every plan, and Google says that should make answers crisper when you’re digging through huge doc piles. At the same time they’ve added a new goal-setting tool so each notebook can lean toward a specific research target.
If the extra context actually works, we could see clearer insights and a smoother back-and-forth. Still, the real benefit probably depends on how often you juggle truly massive files - a short note probably won’t feel much different. The blog calls it a “major boost to performance and quality,” yet we haven’t seen any third-party benchmarks.
It’s also unclear whether the larger window will tax devices or add latency for some users. As the update rolls out, we’ll have to wait for everyday feedback to see if the hype lives up to the numbers. Early testers might spot quicker synthesis of multi-page reports, but the exact speed gain is still up in the air.
For teams with big research archives, the bigger window could mean fewer manual chunking steps.
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