It looks like Rio 3.5 397B could've simply been a semi-failed embezzling of funding

Reddit r/LocalLLaMA News

Summary

An investigation reveals that the Rio 3.5 397B AI model, funded with $100K, was likely a simple merge of Nex N2 Pro without any training, leading to accusations of funding embezzlement.

Here is the chain of events: 1. The model training [received funding](https://tech.yahoo.com/ai/articles/rio-janeiro-built-ai-model-194348372.html) of R$500K (about $100K USD). 2. The [initial model documentation](https://huggingface.co/prefeitura-rio/Rio-3.5-Open-397B/blob/0e1bf540744675baac21d6be4c61ac273add26a5/README.md) claimed that it was a developed on top of Qwen 3.5 397B with fancy training and great improvements. 3. It was discovered that the model was a cheap, [simple merge with Nex N2 Pro](https://www.reddit.com/r/LocalLLaMA/comments/1u5pkg1/nex_claims_rio_35_is_nex_25_pro_in_trench_coat/) *without any further training*. 4. The model readme [was updated](https://www.reddit.com/r/LocalLLaMA/comments/1u5pkg1/comment/ormhpi4/) to admit that it was based on a Nex N2 Pro merge, while still insisting that *additional training still took place*, and they simply uploaded the wrong model. The previously uploaded model was removed from HF. 5. They [tweeted](https://xcancel.com/IplanRio_rj/status/2066693494769348946) (among something that looks like an attempt at damage control) that the final trained model got lost, so they'll have to redo it from scratch. This reads to me like "we pocketed the funding, delivered a fake result, got caught, and now promise to do the actual work to mitigate impact on us".
Original Article

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The RIO Incident

Reddit r/ArtificialInteligence

A dispute has arisen over Rio 3.5 Open 397B, with Nex-AGI claiming it is a blend of their model and Qwen, not independently trained; evidence includes model identity and weight matching.

prefeitura-rio/Rio-3.5-Open-397B

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Rio 3.5 Open 397B is an open-source, frontier-class AI model post-trained from Qwen 3.5 397B, featuring SwiReasoning for dynamic explicit/latent reasoning switching, achieving state-of-the-art performance across agentic coding, reasoning, and multilingual benchmarks.