Image created with gemini-3.1-flash-image-preview with claude-sonnet-4-5. Image prompt: 1980s NORAD war room with large CRT screen showing glowing blue wireframe map of Asia with bright pulsing nodes over China, dark silhouetted operator pointing at screen, red alarm lights flashing, bold red retro sans-serif text QWEN at top, cinematic lighting, high contrast black background, amber and blue vector graphics, foreboding atmosphere
If you like Claude Code/Codex and have 32GB of RAM: please run Qwen3.5-35B-A3B locally. There’s a before and after for local agents: reliable tool calling, stable agentic loops, only 3B active params. Punches way above its weight! Now is the best time to get started with local
https://x.com/victormustar/status/2026624792602808707
Qwen just released Qwen3.5 on Hugging Face A massive 397B parameter multimodal model with only 17B active, rivaling GPT5.2 and Claude 4.5 across benchmarks.
https://x.com/HuggingPapers/status/2025805747385221491
🌐 pplx-embed is @perplexity_ai new collection of state-of-the-art multilingual embedding models optimized for real-world, web-scale retrieval tasks! – Built on Qwen3 w/ diffusion-based pretraining and bidirectional attention – Available at 0.6B and 4B parameters w/ native INT8
https://x.com/alvarobartt/status/2027094524699259162
122B-A10B is really really really good, what in the world
https://x.com/andrew_n_carr/status/2026347588950372752
🚀 Introducing the Qwen 3.5 Medium Model Series Qwen3.5-Flash · Qwen3.5-35B-A3B · Qwen3.5-122B-A10B · Qwen3.5-27B ✨ More intelligence, less compute. • Qwen3.5-35B-A3B now surpasses Qwen3-235B-A22B-2507 and Qwen3-VL-235B-A22B — a reminder that better architecture, data quality,
https://x.com/Alibaba_Qwen/status/2026339351530188939
Qwen3.5-397B-A17B is now a top 7 open model in the Code Arena. It ranks #17 overall, on par with proprietary models like GPT-5.2 and Gemini-3-Flash. The Code Arena is where agentic capabilities are tested for real-world webdev tasks. Congrats to the @Alibaba_Qwen team! 👏
https://x.com/arena/status/2026337606137725363
Unsloth’s quantizations are pure art. 2 bit Qwen-3.5 highest performing local model on the benchmarks I’ve given it. It has vision, can code, full context (256k 8bit) is only 25gb in vram – 36 tokens/s gen – 220 tokens/s prefill I just don’t like GGUF the speeds are trash
https://x.com/0xSero/status/2026223879077712269
What happens when you make an LLM drive a car where physics are real and actions can’t be undone? I ported CARLA, the autonomous driving simulator, to OpenEnv and added training via TRL + HF Spaces In 50 steps, Qwen 0.6B learns to swerve and brake to avoid pedestrians
https://x.com/SergioPaniego/status/2027064485056241971
The Qwen 3.5 Medium Models are in the Arena! 3.5-27B, 3.5-35B-A3B and 3.5-122B-A10B are ready for you in the Text, Vision and Code Arena! Let’s see how they stack up with less compute. Bring your toughest prompts and don’t forget to vote.
https://x.com/arena/status/2026716550812807181
✨ Run it now with SGLang！Chong!
https://x.com/Alibaba_Qwen/status/2026348924433477775
📊With all the Qwen-3.5 scores out for Text, Code and Vision, let’s compare the evolution of Qwen-3.5 (397B-A17B) vs Qwen-3.0 (235B-A22B). This is a +24 rank jump in Text. Specially where Qwen-3.5 gains the most: Text: – Overall (+24: #19 vs #43) – English (+25: #21 vs #46) –
https://x.com/arena/status/2026404630297719100
🔥 Qwen 3.5 Medium Model Series FP8 weights are now open and ready for deployment！ Native support for vLLM and SGLang. Check the model card for example code. ⚡️ Optimize your workflow with FP8 precision. 👇 Get the weights: Hugging Face：
https://x.com/Alibaba_Qwen/status/2026682179305275758
🚩Qwen3.5 INT4 model is now available! https://t.co/rY5GrT3b60 @Alibaba_Qwen @JustinLin610
https://x.com/HaihaoShen/status/2026208062009426209
A big jump in intelligence-per-watt today: “”Qwen3.5-35B-A3B now surpasses Qwen3-235B-A22B-2507″”
https://x.com/awnihannun/status/2026353100144218569
Huge thanks to the @vllm_project for the Day-0 support on the Qwen3.5 Medium Series 🚀
https://x.com/Alibaba_Qwen/status/2026496673179181292
Minimax M2.5 GGUFs (from Q4 down to Q1) perform poorly overall. None of them come close to the original model. That’s very different from my Qwen3.5 GGUF evaluations, where even TQ1_0 held up well enough. Lessons: – Models aren’t equally robust, even under otherwise very good
https://x.com/bnjmn_marie/status/2027043753484021810
Qwen 3.5 family is here! > vision built-in, and can outperform previous VL models > designed to be more efficient > expanded support for more languages 35B: (fits on 24GB+ system) ollama run qwen3.5:35b 122B: ollama run qwen3.5:122b 397B (cloud only): ollama run
https://x.com/ollama/status/2026598944177009147
Qwen3.5-35B-A3B is now in Jan 🔥
https://x.com/Alibaba_Qwen/status/2026660582221558190
Qwen3.5-35B-A3B is now live in LM Studio 🚀
https://x.com/Alibaba_Qwen/status/2026496880285462962
Taken at face value, this is… somewhat catastrophic for MoEs, as @YouJiacheng notes. By right, a 397B-A17B ought to have a higher “”power level”” than a dense 27B. Also a big W for Qwen’s integrity and HLE eval quality, I guess. 397B is certainly better at memorization.
https://x.com/teortaxesTex/status/2026690994029072512
the conclusion should not be about moe vs dense, but that you can “”benchmaxx”” (not always a bad thing btw) HLE with tools no matter the model size the difference between Qwen3.5-35B-A3B and Qwen3.5-397B-A17B is only 1 point
https://x.com/eliebakouch/status/2026727151978840105
The new Qwen3.5 Medium models are ready to run 🔥 GGUF support is here! Big thanks to @UnslothAI for making it happen so quickly 🚀
https://x.com/Alibaba_Qwen/status/2026497723944546395
The Qwen3.5 series maintains near-lossless accuracy under 4-bit weight and KV cache quantization. In terms of long-context efficiency: Qwen3.5-27B supports 800K+ context length Qwen3.5-35B-A3B exceeds 1M context on consumer-grade GPUs with 32GB VRAM Qwen3.5-122B-A10B supports
https://x.com/Alibaba_Qwen/status/2026502059479179602
Why benchmarks like Peter’s “”Bullshit Benchmark”” or my ShizoBench matter so much and what do Strawberries have to do with it? I was very skeptical of the performance of Qwen3.5-27B on ArtificialAnalysis leaderboard. So I’m testing the model myself a bit. Naturally I tried the
https://x.com/scaling01/status/2027110908775002312
Qwen3.5-397B-A17B is currently the #1 trending model on Hugging Face. 🏆 This flagship open-weight model is designed for high-performance inference and complex reasoning. 🚀 Try it now on Hugging Face: https://x.com/Ali_TongyiLab/status/2026211680653611174
NVIDIA just released a Blackwell-optimized Qwen3.5 MoE on Hugging Face 397B parameters quantized to NVFP4 for 2x faster inference with SGLang.
https://x.com/HuggingPapers/status/2025825405836648849
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