Opening article
Following January 2025's DeepSeek Moment, China's open-source AI ecosystem has increasingly focused on architectural preferences, modality diversification, and domestic hardware adoption. Leading models like Kimi K2, MiniMax M2, and Qwen3 have largely shifted toward Mixture-of-Experts architectures to control costs and manage real-world constraints. Meanwhile, developers are releasing full engineering assets alongside model weights to enable sustainable, flexible deployment.
This is the second blog in a three-part series on China's open source community's historical advancements since January 2025's "DeepSeek Moment." The first blog is available here , and the third blog is available here .
In this second piece we turn our focus from models to the architectural and hardware choices Chinese companies have made as openness becomes the norm.
For AI researchers and developers contributing to and relying on the open source ecosystem and for policymakers understanding the rapidly changing environment, architectural preferences, modality diversification, license permissiveness, small model popularity, and growing adoption of Chinese hardware point to leadership strategies across a multitude of paths. DeepSeek R1's own characteristics inspired overlap and competition, and contributed to heavier focus on domestic hardware in China.
Starting in February 2025, open-source activity was no longer focused only on text models. It quickly expanded into multimodal and agent-based directions: Any-to-Any models, text-to-image, image-to-video, text-to-video, TTS, 3D, and agents all progressed in parallel. What the community pushed forward was not just model weights, but a full set of engineering assets, including inference deployment, datasets and evaluation, toolchains, workflows, and edge-to-cloud coordination. The parallel emergence of video generation tools, 3D components, distillation datasets, and agent frameworks pointed to something larger than isolated breakthroughs---it pointed to reusable system-level capabilities.
In 2025, model releases increasingly aligned with inference frameworks, quantization formats, serving engines, and edge runtimes. A prominent goal was no longer just to make weights downloadable, but to ensure that models could run directly on target domestic hardware---and run reliably and efficiently. This change was most visible on the inference side. For example, With DeepSeek-V3.2-Exp , both Huawei Ascend and Cambricon chips achieved day-zero support, not as cloud demos, but as reproducible inference pipelines released alongside the weights, enabling developers to validate real-world performance directly.
In the past year, leading models from the Chinese community had almost unanimously moved toward Mixture-of-Experts (MoE) architectures , including Kimi K2 , MiniMax M2 , and Qwen3 . R1 itself was an MoE model, it also proved a crucial point: strong reasoning could be open, reproducible, and engineered in practice. Under China's real-world constraints, maintaining high capability while controlling cost, and ensuring models could be trained, deployed, and widely adopted, MoE emerged as a natural solution.
MoE is like a controllable compute distribution system; under a single capability framework, compute resources are allocated across requests and deployment environments by dynamically activating different numbers of experts according to task complexity and value. More importantly, it does not require every inference to consume the full set of resources, nor does it assume that all deployment environments share identical hardware conditions.
The overall direction of Chinese open-source models in 2025 was clear: not necessarily the strongest possible performance, but ability to operate sustainably, deploy flexibly, and evolve continuously, achieving the best cost performance balance.
The competition to lead similar to DeepSeek in a non-text modality heated up. StepFun's released high performance multimodal models, excelling in audio , video , and image generation and processing or editing. Their latest speech-to-speech model Step-Audio-R1.1 boasts state-of-the-art performance, beating proprietary models. Tencent also reflected this shift through open-source work in video and 3D. Its Hunyuan Video models and projects such as Hunyuan 3D reflect growing competition beyond text-centric models.
The shift toward integrated engineering assets, domestic hardware alignment, and cost-effective Mixture-of-Experts architectures highlights how open-source ecosystems are evolving beyond raw model performance toward sustainable, scalable infrastructure.