Alibaba's Qwen4 looks close: new GitHub code points to an imminent launch

What this is about

Code for Alibaba's upcoming Qwen4 family is increasingly showing up in several key software libraries for running AI models. Startup Fortune reports that these aren't leaks. They are infrastructure catching up to an architecture Alibaba has already shown publicly. There is no official date for Qwen4.

What the code shows

Support for the "Qwen4-Exp" architecture appeared in major inference repositories in late August. (Inference means running a finished model.) In llama.cpp, a pull request titled "model : add Qwen4-Exp" (PR #27739, from a contributor named JJJYmmm) was closed without being merged. A different implementation, PR #27742 ("model: add Qwen3.8-Flash-Next (qwen4exp)"), was merged on August 27. Hugging Face Transformers also lists Qwen4-Exp as the architecture of the released checkpoint. The Model Card lists Transformers, vLLM and SGLang as compatible.

Alibaba released Qwen3.8-Flash-Next on August 28 (elsewhere the article says August 26, so the dates contradict each other). It described the model itself as an experimental preview of the architecture that Qwen4 is meant to use. The article describes it as an open model with 125 billion parameters.

What Alibaba has announced

At the Apsara Conference in Hangzhou on September 22, Qwen project lead Liu Dayiheng showed four names under the Qwen4 banner: Max, Plus, Flash and 27B. According to the written announcement, Qwen4 is "currently in training." Conference reports say Liu described its arrival as "very soon." No Model Card, license, benchmarks or date have been published. It is unconfirmed whether the 27B tier will get downloadable weights. For the later Qwen4.5 and Qwen5 series, Alibaba floated models with 5 to 10 trillion parameters.

What you can use today

For developers, what matters is what fits on your own GPU. According to the article, that is currently Qwen3.8-27B, released on August 14 under the Apache 2.0 license. It has 27 billion parameters and is a "dense" model (all parameters are active on every request, unlike Mixture-of-Experts). Its context window is 262,144 tokens, extendable to one million. According to the article, dense models of this size run predictably on a single high-end consumer or workstation GPU.

Competitive pressure from Moonshot

The article sees the real competition not at OpenAI or Anthropic, but at Moonshot AI. Its model Kimi K3 (2.8 trillion parameters, Mixture-of-Experts, 1M-token context) launched in July, with the weights following on July 27. According to Axios, it beat Anthropic's "Fable 5" and OpenAI's "GPT-5.6 Sol" in Arena's blind tests for frontend coding. It is the first open model in the 3-trillion-parameter class. Shortly before the Flash-Next release, Alibaba also raised $10.2 billion in Hong Kong, soon after its worst trading day since early 2025.

Community reaction

On r/LocalLLaMA, posts about Qwen4 and the pull requests drew hundreds of votes. A post on r/Qwen_AI claimed that releases would begin in late October and continue into November. That is unverified.

Our take

Here is what is established: the architecture has a name, it has been previewed publicly, and open-source maintainers have built support for it. Still open are the date, the license, the performance and which Qwen4 models will get open weights. If you want to build a product on an open model now, the article recommends Qwen3.8-27B. Qwen4 27B is one to watch. The source is a small outlet, and some details (the date, details on competitors) are partly contradictory and haven't been independently verified.