Image created with gemini-3.1-flash-image-preview with claude-opus-4.7. Image prompt: Using the provided reference image, preserve every detail — the marigold backdrop, the seated woman’s closed-eyes smile and purple windbreaker, the beanied tattooed singer’s pose, lighting, and framing — but replace only the black handheld microphone with a traditional Chinese red silk lantern held the same way at his mouth, its golden tassel hanging below his grip and its inner candle casting a soft warm glow on his lips, photographically realistic and seamlessly integrated. After generating the image, overlay the text “Alibaba” in the upper-left corner of the frame in large, bold, all-caps ITC Avant Garde Gothic Pro Medium (or a near-identical geometric sans-serif if unavailable), pure white (#FFFFFF), with no date, subtitle, drop shadow, or outline. The text should be substantial in scale — taking up a meaningful portion of the upper-left area — with comfortable margin from the top and left edges, set against the negative space of the orange backdrop so it does not overlap or obscure the singer, the seated woman, or the replaced object.
Sub-32B open weights models now offer GPT-5 level intelligence with Qwen3.5 27B (Reasoning) matching GPT-5 (medium) at 42 and Gemma 4 31B (Reasoning) matching GPT-5 (low) at 39 on the Artificial Analysis Intelligence Index @Alibaba_Qwen’s Qwen3.5 and @GoogleDeepMind’s Gemma 4
https://x.com/ArtificialAnlys/status/2043929874537296026
⚡ Meet Qwen3.6-35B-A3B：Now Open-Source！🚀🚀 A sparse MoE model, 35B total params, 3B active. Apache 2.0 license. 🔥 Agentic coding on par with models 10x its active size 📷 Strong multimodal perception and reasoning ability 🧠 Multimodal thinking + non-thinking modes
https://x.com/Alibaba_Qwen/status/2044768734234243427
LM Performance：Qwen3.6-35B-A3B outperforms the dense 27B-param Qwen3.5-27B on several key coding benchmarks and dramatically surpasses its direct predecessor Qwen3.5-35B-A3B, especially on agentic coding and reasoning tasks.
https://x.com/Alibaba_Qwen/status/2044768738294268199
VLM Performance：Qwen3.6 is natively multimodal, and Qwen3.6-35B-A3B showcases perception and multimodal reasoning capabilities that far exceed what its size would suggest, with only around 3 billion activated parameters. Across most vision-language benchmarks, its performance
https://x.com/Alibaba_Qwen/status/2044768742761189762
Alibaba released Qwen3.6-35B-A3B today. Big jump compared to Qwen 3.5-35B model. It’s a sparse MoE, 35B total params, only 3B active. Natively multimodal, thinking and non-thinking modes. Hardfacts: SWE-bench Verified: 73.4, near dense Qwen3.5-27B (75.0), way ahead of
https://x.com/kimmonismus/status/2044780695361290347
Qwen 3.6 is here, and open-source! Run it locally with improved agentic coding capabilities. Try it with Claude Code: ollama launch claude –model qwen3.6 Try it with OpenClaw: ollama launch openclaw –model qwen3.6 Run it: ollama run qwen3.6
https://x.com/ollama/status/2044779844672852465
Shocking result on my pelican benchmark this morning, I got a better pelican from a 21GB local Qwen3.6-35B-A3B running on my laptop than I did from the new Opus 4.7! Qwen on the left, Opus on the right
https://x.com/simonw/status/2044830134885306701
🎉 Congrats @Alibaba_Qwen on the first open-weight Qwen3.6! Stronger agentic coding and a new thinking preservation option to retain reasoning context across turns. Same architecture as Qwen3.5, so serving teams can upgrade in place. Day-0 support in vLLM v0.19+. Thinking, tool
https://x.com/vllm_project/status/2044787721538060784
Introducing Nucleus-Image: the first sparse Mixture-of-Experts diffusion model 17B parameters. Only 2B active. 10x more parameter-efficient than leading diffusion models. Toe-to-toe with GPT Image 1, Imagen 4, and Qwen-Image: from pure pre-training alone. No DPO. No RL. No
https://x.com/withnucleusai/status/2044412335473713284
Qwen/Qwen3-Coder-Next · Hugging Face
https://huggingface.co/Qwen/Qwen3-Coder-Next
We built FrogsGame as a new task for evaluating AI’s posttraining skills! It’s a tool-using RL environment built around a blind-start interaction loop. Frontier agents get a container with the Qwen3-8B tokenizer, board-generating scaffolding, and @tinkerapi for remote training
https://x.com/karinanguyen/status/2044885375085339023
2-bit Qwen3.6-35B-A3B did a complete repo bug hunt with evidence, repro, fixes, tests and a PR writeup. 🔥 Run it locally in Unsloth Studio with just 13GB RAM. 2-bit Qwen3.6 GGUF made 30+ tool calls, searched 20 sites and executed Python code. GitHub:
https://x.com/UnslothAI/status/2044858346948464743
Qwen3.6-35B-A3B can now be run locally!💜 The model is the strongest mid-sized LLM on nearly all benchmarks. Run on 23GB RAM via Unsloth Dynamic GGUFs. GGUFs to run:
https://t.co/VlyW8UwDjw Guide:
https://x.com/UnslothAI/status/2044786492451778988
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