The trouble started on May 11th, when a LocalLLaMA user posted a question that sounds simple but cuts deep: why does Qwen3.6-35B-A3B feel noticeably less capable than the version before it? Not a benchmark anomaly. Not a theoretical regression. A practitioner running models locally noticed that a newer version number was producing worse outputs, and they wanted to know why. That post scored a modest 6.4, suggesting the observation resonated with enough people to bubble up without going viral. Then, eight days later, a second signal arrived — a direct performance comparison between Qwen3-Coder-Next and Qwen3.6-27B-MTP, both run on Strix Halo hardware in quantized form. The framing here shifted from complaint to investigation, with someone actually putting two versions side by side in a controlled-enough environment to draw comparisons. Together, these two posts sketch the outline of a community quietly losing confidence in the versioning logic behind Qwen's rapid release cadence.
If confirmed, here is what this means. Alibaba's Qwen team has been releasing model variants at a pace that is genuinely impressive — and apparently, at a pace that is outrunning quality control on at least some capability dimensions. For developers building local applications on top of Qwen models, version number is supposed to be a reliable proxy for capability. When it isn't, every upgrade decision becomes a gamble. The second-order effect is trust erosion: the open-weights community is unusually attentive to exactly this kind of regression, and word travels fast. If Qwen 3.6 is genuinely underperforming Qwen 3.5 on practical tasks despite the higher version number, that creates a real documentation and communication problem for Alibaba — particularly as it competes for developer mindshare against Mistral, Meta, and others who are also pushing frequent updates. The MTP (multi-token prediction) architecture variant adds another layer of complexity, since comparing MTP models to standard ones isn't apples-to-apples, and many users may not realize that.
Watch for official Qwen release notes or community moderator posts that address the versioning confusion directly — and pay particular attention to whether benchmark scores on coding tasks specifically show a gap between the 3.5 and 3.6 generations.
<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1s7rcwr/qwen35_omni_qwens_latest_generation_of_fully/"> <img alt="Qwen3.5 Omni - Qwen’s latest generation
<!-- SC_OFF --><div class="md"><p>Got Qwen 3.5 27B to 1,103,941 tok/s on 12 nodes with 96 B200 GPUs.</p> <p>At that rate you process 50,000 insurance policy documents in hours
<!-- SC_OFF --><div class="md"><p>I'm using this <a href="https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-GGUF">opus 4.6 distilled version of
<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks
<!-- SC_OFF --><div class="md"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat
<!-- SC_OFF --><div class="md"><p>I could not find good data points on what speed one could get with a single 5090 and enough DDR4 RAM. </p> <p>My system: AMD EPYC 7532 32core
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1srhtvg/where_we_are_in_a_year_everything_has_changed/"> <img alt="Where we are. In a year, everything has
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1sn3izh/qwen3635ba3b_released/"> <img alt="Qwen3.6-35B-A3B released!"
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