Brazilian Municipal IT Company's Rio 3.5 Model Exposed as Wrapper of Domestic Nex and Qwen

An IT company under the Rio de Janeiro city government recently open-sourced a 397B-parameter large model named Rio 3.5, which achieved SOTA results on multiple benchmarks, even surpassing Alibaba's Qwen 3.7 Plus, causing a stir in the community. However, the Nex-AGI team initiated by Shanghai Chuangzhi Institute quickly released evidence showing that Rio 3.5 is actually a fusion of Nex N2 Pro and Qwen 3.5.

Evidence of Wrapping

The Nex team provided two key pieces of evidence on GitHub:

• Model Self-Identification Test: After removing Rio's hardcoded system prompts, the model was asked "Who are you?" 120 times. 79% of responses claimed to be "Nex", 73% mentioned "Nex-AGI", and never identified as "Rio". The model could also verbatim recite Nex's institutional introduction, such as "Large Model Ecosystem Alliance" and "Shanghai Chuangzhi Institute".
• Weight Analysis: Each of Rio's 60 layers was verified, showing that every weight tensor precisely lies on the line between Nex and Qwen, with a stable mixture ratio of approximately 0.57 Nex plus 0.43 Qwen. The collinearity cos_fit ranged from 0.984 to 0.993, with statistical deviations of thousands of standard deviations, ruling out the possibility of independent training coincidence.

Aftermath

Faced with irrefutable evidence, the Rio team removed the model from HuggingFace, leaving only an apology statement acknowledging the use of Nex and Qwen to build the model, but claiming that "the wrong version without final distillation was uploaded". The Nex team emphasized that the open-source community must adhere to the baseline of attribution and acknowledgment.

Similar Cases

This is not the first time a domestic model has been wrapped:

• Cursor Composer 2 (March 2025): A self-proclaimed self-developed code model was exposed to have API request paths containing "kimi", later admitting to using Kimi through the Fireworks AI platform, but without mentioning it at release.
• Stanford Llama3-V (2024): Claimed to train a model surpassing GPT-4V for only $500, but its code and weights were nearly identical to Tsinghua's MiniCPM-Llama3-V 2.5, even reproducing the same recognition errors on unpublished "Tsinghua Bamboo Slips".

These incidents show that wrapping domestic models is common across universities, enterprises, and official institutions.