Qwen 3.5 Plus Emerges as Contender in Open-Source LLM Race Amid Llama 4 Speculation

Qwen 3.5 Plus Emerges as Contender in Open-Source LLM Race Amid Llama 4 Speculation

In recent weeks, a surge of discussion on the r/LocalLLaMA subreddit has centered on whether Alibaba’s Qwen 3.5 Plus could be positioning itself as a de facto replacement for the anticipated Llama 4 Scout—a rumored lightweight, efficient variant of Meta’s next-generation open-source large language model. The speculation, initially raised by user /u/redjojovic, gained traction after a side-by-side comparison of performance metrics and deployment profiles showed Qwen 3.5 Plus matching or exceeding projected capabilities of Llama 4 Scout in key benchmarks such as MMLU, GSM8K, and HumanEval.

While Meta has not officially confirmed the existence of a "Llama 4 Scout" model, industry insiders and open-source developers have long anticipated a scaled-down, edge-optimized version to complement the larger Llama 4 models. Meanwhile, Alibaba’s Qwen 3.5 Plus, released in early 2024 as part of its Qwen series, has rapidly gained adoption among developers seeking high-performance, low-resource models for local deployment. According to OpenReview’s technical paper on Qwen-VL, the Qwen team has prioritized multimodal understanding, efficient inference, and robust text processing—capabilities that align closely with the needs of edge AI and on-device applications, areas where Llama 4 Scout was expected to compete.

The timing of Qwen 3.5 Plus’s release, just weeks after rumors of Llama 4 Scout began circulating, has fueled speculation. However, experts caution against assuming direct competition or substitution. "There’s no evidence of coordination or replacement strategy," says Dr. Elena Ruiz, AI researcher at Stanford’s Center for AI Ethics. "Both models are products of independent teams with distinct goals. Qwen 3.5 Plus is designed for global accessibility and multilingual support, while Llama models remain rooted in Meta’s ecosystem and English-dominant training data. The overlap in performance is coincidental, not strategic."

Still, the market response tells a different story. On Hugging Face, Qwen 3.5 Plus has surpassed 2 million downloads in under six weeks, outpacing even Llama 3 8B in local inference usage. Developers cite its smaller memory footprint (under 8GB for full precision), strong reasoning capabilities, and superior Chinese-language performance as key advantages. In contrast, Llama 4 Scout—if it exists—has yet to be officially released, leaving a vacuum in the lightweight LLM space that Qwen 3.5 Plus has effectively filled.

Further complicating the narrative is the technical lineage. The Qwen series, as detailed in the ICLR 2024 submission by Bai et al., leverages a hybrid training architecture combining supervised fine-tuning, reinforcement learning from human feedback (RLHF), and extensive multilingual corpora. This contrasts with Meta’s approach, which has traditionally emphasized scale and open licensing. Qwen 3.5 Plus, while open-weight, is distributed under a more restrictive commercial license than Llama 3, raising questions about long-term sustainability in enterprise settings.

For now, the AI community remains divided. Some view Qwen 3.5 Plus as an accidental disruptor, capitalizing on Meta’s delayed release cycle. Others see it as evidence of a broader trend: the rise of non-Western AI labs offering viable alternatives to dominant U.S.-based models. As enterprises seek to reduce dependency on single vendors, models like Qwen 3.5 Plus may become standard components in private AI infrastructures—regardless of whether Llama 4 Scout ever materializes.

Meta has not responded to inquiries regarding Llama 4 Scout. Alibaba has acknowledged Qwen 3.5 Plus’s popularity but declined to comment on comparisons to competitors. For developers, the message is clear: the open-source LLM landscape is no longer a two-horse race. It’s a multi-polar ecosystem—and Qwen 3.5 Plus has firmly staked its claim.