RedNote joins China’s open-source AI wave with the launch of dots.llm1

Chinese social media platform RedNote has released its first open-source large language model, dubbed “dots.llm1,” joining a growing wave of Chinese technology companies pursuing open-source AI strategies that challenge Western proprietary models.

The model, developed by RedNote’s internal Humane Intelligence Lab, activates 14 billion parameters out of a total of 142 billion when responding to queries — an architecture designed to balance performance with cost-efficiency.

The company achieved performance comparable to Alibaba’s Qwen2.5-72B after pretraining on 11.2 trillion high-quality tokens without synthetic data, according to the model’s description on Hugging Face.

RedNote, known domestically as Xiaohongshu, operates one of China’s most influential social commerce platforms with over 300 million monthly active users.

A strategic divergence, not just technology

While OpenAI and Google keep their best AI models locked away, Chinese tech companies are taking the opposite approach — giving away their technology for free. But this isn’t just about different business models, according to industry experts.

“Chinese firms like RedNote are deploying open-source LLMs not just as models but as instruments of ecosystem control and geopolitical leverage. Meanwhile, Western firms such as OpenAI and Google remain committed to proprietary architectures,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “This is no longer a tactical split in model licensing — it’s a structural divergence in trust frameworks, one that will define the next generation of enterprise AI procurement.”

The split goes deeper than technology choices. “Western AI leaders are optimizing for shareholder return, compliance insulation, and platform lock-in through closed API-delivered models. In contrast, Chinese vendors like RedNote and DeepSeek are aggressively open-sourcing to expand national influence, cultivate developer mindshare, and drive localization-led adoption,” Gogia explained.

Performance vs. purpose

So, how does RedNote’s AI stack up? According to another detailed document on Github, dots.llm1 scored 56.7 on C-SimpleQA, a test of Chinese language skills — not quite as high as DeepSeek-V3’s 68.9, but respectable for a newcomer.

However, some analysts question whether RedNote is playing to its strengths. “Rednote, which has pioneered content-driven commerce, is well-positioned to build a large model based on the ton of data its ecosystem generates,” said Neil Shah, VP for research and partner at Counterpoint Research. “However, in this race of LLMs, Rednote would be better off building a more targeted model to drive AI-driven commerce sitting on a gold mine of data around users’ likes, dislikes, purchase behaviours, etc.”

The economics of free AI

The model uses a “mixture of experts” design that activates only the parts it needs for each task, making it cheaper to run than traditional models. But the economics go beyond technical efficiency.

“RedNote’s dots.LLM1 is less a revenue product and more a market accelerant,” Gogia explained. “The open-source model isn’t a broken business plan — it’s a strategic play to become foundational infrastructure across sovereign cloud ecosystems and public developer communities. The long game is not monetization through licensing but platform entrenchment through adoption.”

This approach is enabled by structural advantages. “Chinese firms benefit from central government subsidies, national procurement incentives, and policy exemptions that support loss-leader behavior in the short term. What might be financially unsustainable in the West becomes strategically viable in China due to alignment with state AI priorities,” Gogia said.

For Chinese vendors, open-source LLMs serve as soft power tools, exporting not only code but also embedded ideologies and governance frameworks into markets across Southeast Asia, Africa, and Latin America.

From social media to a global AI player

RedNote’s AI ambitions go beyond just releasing models. The company has already put AI to work on its main platform with Diandian, a search tool that helps users find content among the platform’s vast trove of lifestyle posts. With nearly 600 million daily searches—about half of what Google rival Baidu handles—RedNote has a real testing ground for its AI.

The timing isn’t coincidental. RedNote grabbed international headlines earlier this year when more than three million Americans joined the platform during TikTok uncertainty, giving the company a taste of global reach. Last week, it opened its first office outside mainland China in Hong Kong.

The trust challenge

For enterprises considering these open-source alternatives, the calculation isn’t straightforward.

“The core trade-off for enterprise buyers is no longer just cost versus performance — it is transparency versus control,” Gogia noted. “Controlled open LLMs provide a bridge across this chasm, giving CIOs and CISOs the ability to audit, customize, and self-host AI models without being beholden to proprietary ecosystems. However, the burden of governance shifts inward — enterprises must build their own trust scaffolding to make openness production-grade.”

The geopolitical dimension adds another layer of complexity. “Chinese open-weight models like dots.llm1 may be technically transparent—but in the eyes of global enterprises, transparency is no substitute for trust. Especially in regulated industries — banking, healthcare, and defense — geopolitical risk is now baked into AI architecture decisions,” Gogia warned.

What this means for business

The rise of capable, free AI models from Chinese companies is forcing a rethink of enterprise AI strategy. Companies that once assumed they’d need to pay premium prices for cutting-edge AI now have alternatives that might work just as well for many tasks, but with new considerations around governance, geopolitics, and long-term strategy.

RedNote’s entry into open-source AI represents more than technological competition — it’s part of a fundamental shift in how AI power will be distributed globally. While these models offer compelling technical capabilities at attractive price points, enterprises must weigh transparency benefits against new governance challenges and geopolitical considerations.