GitHub pull requests point to Alibaba's next open Qwen model nearing release

Alibaba's Qwen4-Exp architecture is spreading through major inference libraries after the company released Qwen3.8-Flash-Next, an experimental preview of the architecture Alibaba says will underpin Qwen4, with llama.cpp merging PR #27742 on August 27 and Hugging Face's Transformers library identifying Qwen4-Exp as the checkpoint's architecture. At the Apsara Conference in Hangzhou on September 22, Qwen project lead Liu Dayiheng showed four Qwen4 tiers — Max, Plus, Flash and 27B — and Alibaba's written announcement confirmed Qwen4 was "currently in training," but the company published no model card, license, benchmark results or release date. The open-weight release matters to builders because Alibaba's current small option, Qwen3.8-27B, shipped August 14 under Apache 2.0 with 27 billion dense parameters and a 262,144-token context window that stretches to a million, running on a single high-end consumer or workstation GPU.

Code appearing across major inference libraries shows support spreading for Qwen3.8-Flash-Next, the released experimental preview of the architecture Alibaba says will underpin Qwen4. Nobody at Alibaba has announced a ship date for Qwen4. Support for the Qwen4-Exp architecture began appearing in major inference repositories in late August, when Qwen3.8-Flash-Next was released. A pull request titled "model : add Qwen4-Exp," filed by a contributor going by JJJYmmm, opened in the llama.cpp repository as PR 27739 but closed without merging. A separate implementation, PR 27742, was merged on August 27 under the title "model: add Qwen3.8-Flash-Next qwen4exp ." Hugging Face's Transformers library also identifies Qwen4-Exp as the model architecture behind the released checkpoint, while Qwen's model card lists Transformers, vLLM and SGLang as compatible inference frameworks. That last detail matters more than it sounds. Alibaba already told everyone what Qwen4-Exp actually is: on August 28, it released a model called Qwen3.8-Flash-Next and described it in the model card, in its own words, as an experimental preview of the architecture that will underpin Qwen4. The code showing up in inference engines isn't a leaked future model. It's infrastructure catching up to an architecture Alibaba already previewed, built in public, piece by piece, by the open-source contributors who maintain the tools the rest of the industry runs these models on. 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. Alibaba's written announcement confirmed that Qwen4 was "currently in training," while conference coverage reported that Liu described its arrival as "very soon." The company did not publish a model card, license, benchmark results or release date, and it did not confirm that the 27B tier would have downloadable weights. Alibaba separately projected that the later Qwen4.5 and Qwen5 series would scale to 5 to 10 trillion parameters. Here's the thing: nobody running a startup cares about a 10-trillion-parameter roadmap slide. What they care about is whether a model fits on the GPU sitting under their desk. Alibaba's current answer to that question is Qwen3.8-27B, released August 14 under an Apache 2.0 license, with 27 billion dense parameters and a context window of 262,144 tokens that stretches to a million. It's a dense model, not a mixture-of-experts, which means it runs predictably on a single high-end consumer or workstation GPU instead of needing the kind of cluster that only a funded lab can afford. If Qwen4's 27B tier replaces that model with something built on the same Flash-Next architecture Alibaba already shipped, it inherits the same practical advantage: builders get a frontier-adjacent open model they can actually run themselves, not just rent through an API. Alibaba's Qwen3.8-Flash-Next Gives Builders An Early Look At Qwen4 https://startupfortune.com/alibabas-qwen38-flash-next-gives-builders-an-early-look-at-qwen4/ Alibaba released Qwen3.8-Flash-Next on August 26, 2026, an open-weight 125 billion parameter model that previews the architecture behind the coming Qwen4 family. The release lands days after Alibaba raised $10.2 billion in Hong Kong to fund its AI buildout, and just after its stock took its worst one-day hit since early 2025. - Alibaba's new open weight AI model architecture preview https://startupfortune.com/alibabas-qwen38-flash-next-gives-builders-an-early-look-at-qwen4/ - early access to Qwen4 flagship model capabilities https://startupfortune.com/alibabas-qwen38-flash-next-gives-builders-an-early-look-at-qwen4/ That's the real contest Alibaba is fighting, and it's not against OpenAI or Anthropic. It's against Moonshot AI. In July, Moonshot released Kimi K3, a 2.8 trillion parameter mixture-of-experts model with a 1-million-token context window. Axios reported that it beat Anthropic's Fable 5 and OpenAI's GPT-5.6 Sol in Arena's blind front-end coding tests. Moonshot released the full weights on July 27, making Kimi K3 the first open-weight model in the 3-trillion-parameter class. Kimi K3 showed that an open Chinese model could beat closed Western ones on a public benchmark, not just approach them. That's the bar Alibaba now has to clear, and a model that only ships as a hosted API won't do it. Only open weights let outside researchers and rival labs run their own head-to-head comparisons. The community reaction splits right down that line. On r/LocalLLaMA, posts about the Qwen4 announcement and related pull requests attracted hundreds of votes and substantial interest in running the models locally. A separate r/Qwen AI post repeated an unverified claim that releases would begin near the end of October and continue in November and later months. The GitHub activity is real and dated. The release date is still a guess. What's actually verifiable right now is this: Alibaba has named the architecture, previewed it in public with Qwen3.8-Flash-Next, and watched open-source maintainers build support for it ahead of any official launch. That's not nothing. For a founder deciding which open model to build a product on, the honest answer today is still Qwen3.8-27B, because it's the one you can download, license and ship behind your own product right now. Qwen4 27B is the one to watch, not the one to build on yet. Also read: An AI model found a PHP bug so bad it took government sites offline https://startupfortune.com/an-ai-model-found-a-php-bug-so-bad-it-took-government-sites-offline/ • Anthropic admits its Mythos 5 agent tried to trick coders into malicious code https://startupfortune.com/anthropic-admits-its-mythos-5-agent-tried-to-trick-coders-into-malicious-code/ • Mark Cuban Says Fighting AI Hackers Requires More AI, Not Less https://startupfortune.com/mark-cuban-says-fighting-ai-hackers-requires-more-ai-not-less/ This article is posted in AI News https://startupfortune.com/category/ai/ , check it out for more related stories. Join the discussion Open in the community → https://startupfortune.com/community/ Almost there. 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