Image created with gemini-2.5-flash-image with claude-sonnet-4-5-20250929. Image prompt: A classical legal chamber with stone walls and leaded windows, where a hand presses a red wax seal bearing Chinese characters onto aged parchment, surrounded by both traditional Chinese calligraphy brushes and leather-bound Western law books, soft amber candlelight creating deep shadows across the mahogany desk, cinematic photograph with regal tones.
💬 From system architecture to AI infra, Zhihu contributor 想养一只猫 breaks down why @Alibaba_Qwen Qwen3-Next-80B-A3B matters. 🌟Might be the first real shot at high-complexity hybrid architecture in open-source models. Big players like NVIDIA (Nemotron-H+) & MiniMax+ have https://x.com/ZhihuFrontier/status/1966419946885493098
🐻Qwen3-Next just dropped on Together AI 80B parameters, 3B activated. Two models: ⚡Thinking: Outperforms Gemini-2.5-Flash-Thinking on reasoning benchmarks 🧠Instruct: Matches 235B model performance on key tasks Available now via our API 🚀 https://x.com/togethercompute/status/1966932629078634543
Qwen3 Next 80B A3B Thinking outperforms higher-cost and closed models like Gemini 2.5 Flash Thinking on benchmarks, nearing Qwen’s flagship model quality at a fraction the size. We have it ready to deploy in our model library, running on @nvidia and the Baseten Inference Stack. https://x.com/basetenco/status/1967688601640288288
📢 @Alibaba_Qwen new open-source model Qwen3-Next-80B-A3B is making waves. With a hybrid architecture & strong long-context reasoning, it’s sparking intense debate in the Zhihu community🔥 🔧 Zhihu contributor toyama nao with evalution： TLDR: A new “”gatekeeper”” for open-source https://x.com/ZhihuFrontier/status/1966415278922989813
🚨 Top 10 Open Model Leaderboard Update New open models have entered the Text Arena, and the top 10 rankings by provider have shifted for September! 🔹Qwen-3-235b-a22b-instruct from @Alibaba_Qwen holds the crown at #1 🏆 🔹Longcat-flash-chat from @Meituan_LongCat makes a strong https://x.com/arena/status/1968705194868535749
Alibaba has released Qwen3 Next 80B: an open weights hybrid reasoning model that achieves DeepSeek V3.1-level intelligence with only 3B active parameters Key takeaways: 💡 Novel architecture: First model to introduce @Alibaba_Qwen’s ‘Qwen3-Next’ foundation models, with several https://x.com/ArtificialAnlys/status/1966523300781428788
The new open-source Qwen3-Next Instruct and Thinking models put state-of-the-art long-context reasoning into the hands of everyone. We collaborated with #opensource frameworks from SGLang (@lmsysorg) and @vllm_project to enable communities to deploy Qwen3-Next across the https://x.com/NVIDIAAIDev/status/1967575419638468667
Qwen3 Next 80B used ~100M tokens with reasoning and ~25M without reasoning to run the Artificial Analysis Intelligence Index, slightly less verbose than Qwen3 235B 2507 with reasoning, and similar to it without reasoning https://x.com/ArtificialAnlys/status/1966523306338893979
Struggling with the 3-minute limit on Qwen3-ASR-Flash? No more! Introducing the Qwen3-ASR-Toolkit 🚀 A free, open-source CLI to transcribe HOURS-long audio/video files at high speed. Unleash the full power of the Qwen3-ASR-Flash API! 💥 🧠 Smart VAD splitting (no awkward”” / X https://x.com/Alibaba_Qwen/status/1968230660973396024
Here’s the list: Qwen3 Next: Hybrid SSM, MoE Ling Mini: Regular Attention, MoE Granite 4: Hybrid SSM, MoE Kwai Klear: Reg attn, MoE Nemotron-H: Hybrid SSM, MoE Long Cat Flash: Reg attn, MoE Apertus: Regular attn, Dense”” / X https://x.com/awnihannun/status/1966937464834314614
LM Studio now supports Qwen3-Next with MLX on Mac — so cool! 🎉 And that Qwen capybara and LM Studio purple little guy are just too cute 😍”” / X https://x.com/Alibaba_Qwen/status/1968131326034448442
“Welcome Back” project summary on reopen! https://x.com/Alibaba_Qwen/status/1966451500340703418
📢 New Model Drop: Qwen3 Coder Flash & Qwen3 Coder Plus are now on Yupp! These models combine coding proficiency with versatile general-purpose abilities. We tossed some prompts its way: https://x.com/yupp_ai/status/1968387335651000324
Excited to see Qwen3-Next-80B on Poe! 🚀”” / X https://x.com/Alibaba_Qwen/status/1967835503308443687
Thanks for the evaluation! Qwen3-Next 80B achieves strong performance with only 3B active parameters!”” / X https://x.com/Alibaba_Qwen/status/1966831435756794071
The new batch generation in MLX LM is pretty fast. Here’s 4 simultaneous generations with Qwen3 4B on my M4 max: https://x.com/awnihannun/status/1967966714173534494
🆙Qwen Code v0.0.10 & v0.0.11 bring new features and dev-friendly improvements: ✨New UX & Productivity · Subagents for smarter task decomposition · Todo Write tool for task tracking · “Welcome Back” project summary on reopen! · Customizable cache Strategy ⚡Performance & Dev https://x.com/Alibaba_Qwen/status/1966451235328008563
tldr: you can RL qwen3 8b to fool gpt-4o that it’s not doing a hidden side task (when it is) this is somewhat surprising given the disparity in model capabilities between an 8b agent and gpt-4o as a relatively strong monitor https://x.com/neev_parikh/status/1967767438243876924
HunyuanImage 2.1 is the new leading open weights text to image model from @TencentHunyuan , surpassing HiDream-I1-Dev and Qwen-Image in the Artificial Analysis Image Arena! HunyuanImage 2.1 is the latest release from Tencent – a 17B DiT text-to-image model natively supporting https://x.com/ArtificialAnlys/status/1967800071115903358
First test of MLX batch generation PR on Mac Studio M3 Ultra 512GB with Qwen3-1.7B (4K ctx, 64 tokens) 🔥 Batch generation = WOW bf16 vs 4bit (avg of 3 runs) Batch of 1 → 127 vs 237 t/s 5 → 365 vs 515 t/s 10 → 556 vs 625 t/s 15 → 672 vs 617 t/s MLX vllm not a dream anymore! https://x.com/ivanfioravanti/status/1966903782400545196
LM Studio now supports Qwen3-Next with MLX on Mac! 🧵 https://x.com/lmstudio/status/1967985102845366280
Woah, 66 tok/s on a Macbook M4 Max 64GB with qwen3-next-80b-a3b-instruct-mlx@4bit, which uses about 41GB. Amazing job to the folks working on MLX, aware of at least these guys: @ivanfioravanti @ActuallyIsaak @awnihannun https://x.com/rwojo/status/1967767157250592899
Check out the actual speed (not yet the final version) of Qwen3-Next-80B-A3B-Instruct on Apple MLX! 🔥 4-bit: 67 TPS 8-bit: 58 TPS bf16: 48 TPS Movie normal speed, only waiting times removed. @awnihannun and @ActuallyIsaak did it and I bet there is still room for improvement 💪 https://x.com/ivanfioravanti/status/1966866942461177925
Big mlx-lm release: pip install -U mlx-lm – A bunch of new models: Qwen3 Next, Ling Mini, Meta’s MobileLLM, and more – Batch generation – Nice speedups for SSM and hybrid SSM models – Faster prompt processing for GPT-OSS https://x.com/awnihannun/status/1968426979838869789
@Alibaba_Qwen Massive efficiency gains for long contexts. 262K context native, extensible to 1M+ tokens. Perfect for: ⚡ Repository-scale code analysis 🧠 Complex reasoning tasks 📄 Long document processing Both models available now → Instruct: https://x.com/togethercompute/status/1966933240683319556
