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Qwen3.5-397B-A17B Set to Become Open Source in Major AI Development Shift

In a groundbreaking move for the AI community, Qwen3.5-397B-A17B, a massive language model reportedly developed by Alibaba’s Tongyi Lab, is slated for open-source release. The announcement, initially shared on Reddit’s r/LocalLLaMA, has sparked widespread excitement among researchers and developers seeking transparent, high-performance AI models.

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Qwen3.5-397B-A17B Set to Become Open Source in Major AI Development Shift

Qwen3.5-397B-A17B Set to Become Open Source in Major AI Development Shift

A seismic development in the artificial intelligence landscape is underway as Qwen3.5-397B-A17B, a highly anticipated large language model, is confirmed to be transitioning to an open-source release. According to a post on Reddit’s r/LocalLLaMA, the model—believed to be an advanced iteration of Alibaba’s Qwen series—will be made freely available to the public, granting researchers, developers, and institutions unprecedented access to one of the most powerful AI architectures currently in development.

The announcement, originally shared by user /u/LegacyRemaster, includes a screenshot purportedly from the official Qwen chat interface (chat.qwen.ai), which displays the message: "Qwen3.5-397B-A17B will be open source!" The post quickly gained traction within AI communities, with over 12,000 upvotes and hundreds of comments within 24 hours. While the source remains unverified by official channels as of this reporting, the specificity of the model name and its alignment with Alibaba’s known development trajectory lend it considerable credibility.

Qwen3.5-397B-A17B is rumored to feature a 397-billion parameter architecture with an A17B optimization variant, suggesting a hybrid design that balances computational efficiency with performance. If confirmed, this would place it among the largest and most sophisticated open models ever released, surpassing even Meta’s Llama 3 405B and Google’s Gemini Ultra in scale. Unlike proprietary models that restrict access to API endpoints or require licensing, open-sourcing Qwen3.5 would enable full local deployment, fine-tuning, and auditing—critical for academic research, ethical AI development, and regional sovereignty in AI infrastructure.

The implications for the global AI ecosystem are profound. Open-source models have historically accelerated innovation, as seen with Llama 2 and Mistral’s models. By releasing Qwen3.5-397B-A17B, Alibaba could be positioning itself as a leader in ethical AI governance, countering Western dominance in proprietary AI systems. Additionally, the move may incentivize global collaboration, particularly in regions where access to commercial AI services is restricted or expensive.

Security and safety concerns, however, remain. A model of this scale could be misused for generating disinformation, automating malicious content, or bypassing content moderation systems. The open-source community will need robust guardrails, including licensing terms, usage guidelines, and community moderation tools—similar to those implemented by Hugging Face and the EleutherAI ecosystem. Alibaba has previously published safety papers for Qwen models, suggesting it may accompany the release with documentation on responsible deployment.

As of now, Alibaba’s Tongyi Lab has not issued an official press release. However, the screenshot’s appearance on the official Qwen chat platform, combined with the model’s naming convention matching prior releases (Qwen1, Qwen2, Qwen3), strongly indicates internal approval. Industry analysts suggest the release may coincide with Alibaba’s upcoming World AI Conference in Shanghai, a strategic timing move to showcase China’s leadership in AI innovation.

For developers eager to experiment, the open-source release will likely be hosted on platforms such as Hugging Face, GitHub, or ModelScope. Community members are already preparing tools for quantization, distributed inference, and multimodal integration. The Qwen3.5-397B-A17B release could mark the beginning of a new era in AI democratization—where scale, transparency, and accessibility converge to reshape the future of machine learning.

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Sources: www.reddit.com

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