Chinese AI models have emerged as global leaders in 2026, with DeepSeek, Qwen, Kimi, and Doubao rivaling Western counterparts in capability. For researchers and professionals who use these platforms, Notebook Toolkit provides the bridge to save your conversations and research to NotebookLM.
Why Chinese AI Models Matter for Research
Chinese AI models bring unique strengths to the research landscape:
DeepSeek: has become one of the most capable models for coding, mathematics, and technical reasoning. Its R1 model introduced extended chain-of-thought reasoning that produces remarkably detailed problem-solving outputs. Researchers in STEM fields increasingly turn to DeepSeek for its analytical precision.
Qwen (Tongyi Qianwen): by Alibaba offers strong multilingual capabilities with particular strength in Chinese-English bilingual tasks. It excels at translation, cross-cultural analysis, and business research spanning Asian and Western markets.
Kimi: by Moonshot AI pioneered the ultra-long context window, supporting conversations that span hundreds of thousands of tokens. This makes Kimi ideal for analyzing entire books, lengthy reports, or extensive datasets in a single conversation.
Doubao: by ByteDance provides a versatile, user-friendly AI assistant that excels at general-purpose tasks with strong Chinese language understanding. Its integration with ByteDance's ecosystem gives it unique capabilities for content and social media analysis.
Setting Up Notebook Toolkit for Chinese AI Platforms
Getting started is straightforward:
1. Install the Notebook Toolkit extension from the Chrome Web Store
2. Sign in to your Notebook Toolkit account
3. In settings, you can set your preferred language to Chinese if desired (the extension supports both Chinese and English interfaces)
4. Navigate to any supported Chinese AI platform — Notebook Toolkit automatically detects them
Notebook Toolkit currently supports DeepSeek (chat.deepseek.com), Qwen (tongyi.aliyun.com), Kimi (kimi.moonshot.cn), and Doubao (doubao.com). The capture button appears in each platform's interface.
Capturing Chinese-Language Content
Notebook Toolkit handles Chinese text natively. When you capture a conversation in Chinese, every character is preserved perfectly. Mixed Chinese-English conversations — common in technical research — are captured with both languages intact.
Code blocks in DeepSeek conversations maintain their syntax formatting. Qwen's structured outputs and tables are preserved. Kimi's long-context analyses are captured completely, even when they span thousands of lines. Doubao's creative outputs retain their formatting.
Multilingual Research in NotebookLM
The real power emerges when you combine Chinese AI sources with content from other platforms in NotebookLM:
Cross-model comparison: Save the same question answered by DeepSeek, ChatGPT, and Claude. Ask NotebookLM to compare their approaches and identify unique insights from each.
Bilingual research: Save Chinese-language sources alongside English ones. NotebookLM can work with both languages, letting you build a truly multilingual knowledge base.
Technical research: DeepSeek's detailed technical reasoning, combined with Claude's nuanced analysis and ChatGPT's broad knowledge, creates a more complete picture than any single model provides.
Best Practices
Tag your sources by AI model so you can filter them in NotebookLM. Use consistent naming like "DeepSeek — [topic]" or "Qwen — [topic]" for easy identification.
For bilingual research projects, consider creating separate NotebookLM notebooks for Chinese-primary and English-primary sources, then use a third notebook where you combine the most relevant sources from both for final synthesis.
Regular capture is key. Make it a habit to save every valuable AI conversation, regardless of platform. The cumulative knowledge base you build in NotebookLM becomes exponentially more valuable as it grows.