Chinese open AI is forming its own ecosystem
We compared five Chinese model families with Llama, Mistral, and Gemma to test whether model releases are becoming a distinct layer of derivatives, infrastructure, and applications.
This is no longer isolated model export, but it is not yet an evenly mature ecosystem
Chinese families produced 1,830 observed models and 486 GitHub integrations. Applications account for 31.7% of repositories versus 23.7% in the control cohort. Yet usage is concentrated around Qwen and DeepSeek, while the long Kimi and MiniMax tail consists mainly of derivatives with low median traction.
The ecosystem extends beyond models
GitHub cohorts by repository creation month, using the same minimum three-star threshold.
5 Chinese / 3 control families
control: 23.7%
current age-normalized snapshot
non-base releases
Model supply is expanding in waves
Current top-by-downloads snapshot restricted to H1 2026 creation dates.
Absolute counts are not a census of the Hugging Face Hub: each family search is capped at 1,000 current results. The chart describes the observed cohort, not the entire market.
Five Chinese families are developing differently
Current downloads and stars indicate maturity, not historical growth.
Qwen combines a broad derivative layer with high median usage. DeepSeek has fewer model artifacts but the highest median GitHub stars/day. Kimi and MiniMax are proliferating across formats and fine-tunes, but much of that supply still receives little usage.
The control cohort shows a different profile
The control cohort is more infrastructure- and local-distribution-heavy. This is not a model-quality ranking: it compares ecosystem shape around model families, not capability.
What this means for the market
The Chinese stack already has an application layer
Nearly a third of linked GitHub projects are applications, agents, and workflows rather than only runtimes and converters.
Qwen and DeepSeek remain the maturity centers
Cohort breadth does not imply even adoption: the two leaders have markedly stronger traction than the long tail.
Independence remains partial
Distinct derivatives and applications are forming, while local formats and deployment practices remain shared with global open-source infrastructure.
Signals tested by this research
A direct link between AILANTA's early observations and the study's measured findings.
China Full-Stack AI
This signal matches the published research scope: A six-month Hugging Face and GitHub comparison of Qwen, DeepSeek, Kimi, MiniMax, and GLM against Llama, Mistral, and Gemma.
Local AI Runtime
This signal matches the published research scope: A six-month Hugging Face and GitHub comparison of Qwen, DeepSeek, Kimi, MiniMax, and GLM against Llama, Mistral, and Gemma.