🚨 Breaking News Artificial Analysis suddenly introduced an unknown model that surpassed Seedance 2.0. I am uploading few of its result in few mins

🚀 Qwen3.5-Omni is here! Scaling up to a native omni-modal AGI. Meet the next generation of Qwen, designed for native text, image, audio, and video understanding, with major advances in both intelligence and real-time interaction. A standout feature: 'Audio-Visual Vibe Coding'. Describe your vision to the camera, and Qwen3.5-Omni-Plus instantly builds a functional website or game for you. Offline Highlights: 🎬 Script-Level Captioning: Generate detailed video scripts with timestamps, scene cuts & speaker mapping. 🏆 SOTA Performance: Outperform Gemini-3.1 Pro in audio and matches its audio-visual understanding. 🧠 Massive Capacity: Natively handle up to 10h of audio or 400s of 720p video, trained on 100M+ hours of data. 🌍 Global Reach: Recognize 113 languages (speech) & speaks 36. Real-time Features: 🎙️ Fine-Grained Voice Control: Adjust emotion, pace, and volume in real-time. 🔍 Built-in Web Search & complex function calling. 👤 Voice Cloning: Customize your AI's voice from a short sample, with engineering rollout coming soon. 💬 Human-like Conversation: Smart turn-taking that understands real intent and ignores noise. The Qwen3.5-Omni family includes Plus, Flash, and Light variants. Try it out: Blog: qwen.ai/blog?id=qwen3.5-omni Realtime Interaction: click the VoiceChat/VideoChat button (bottom-right): chat.qwen.ai HF-Demo: huggingface.co/spaces/Qwen/Q… HF-VoiceOnline-Demo: huggingface.co/spaces/Qwen/Q… API-Offline: alibabacloud.com/help/en/mod… API-Realtime: alibabacloud.com/help/en/mod…

Which local models can actually handle tool calling? I built a framework to find out. 15 scenarios. 12 tools. Mocked responses. Temperature 0. No cherry-picking. Tested every Qwen3.5 size from 0.8B to 397B, and since some of you asked after the distillation tests: yes, I included Jackrong's Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled too. Only two models went all green: the 27B dense and the distilled 27B. The 397B? Failed two tests. The 122B? Failed one. The 35B? Failed two. The timed-out results — mostly on the smaller models, are cases where the model got stuck in a loop, repeating the same tool call until it hit the 30-second limit. The test that exposed the most models: "Search for Iceland's population, then calculate 2% of it." Simple, but 35B, 122B, and 397B all used a rounded number from memory instead of the actual search result. They didn't trust their own tool output. Small models hallucinate data. Big models ignore data. The 27B just threaded it through.

🔥 Qwen 3.5 Series GPTQ-Int4 weights are live. Native vLLM & SGLang support. ⚡️ Less VRAM. Faster inference. Run powerful models on limited-GPU setups. 👇 Grab the weights + example code: Hugging Face: huggingface.co/collections/Q… ModelScope: modelscope.cn/collections/Qw…

Impressive intelligence density

🚀 Introducing the Qwen 3.5 Small Model Series Qwen3.5-0.8B · Qwen3.5-2B · Qwen3.5-4B · Qwen3.5-9B ✨ More intelligence, less compute. These small models are built on the same Qwen3.5 foundation — native multimodal, improved architecture, scaled RL: • 0.8B / 2B → tiny, fast, great for edge device • 4B → a surprisingly strong multimodal base for lightweight agents • 9B → compact, but already closing the gap with much larger models And yes — we’re also releasing the Base models as well. We hope this better supports research, experimentation, and real-world industrial innovation. Hugging Face: huggingface.co/collections/Q… ModelScope: modelscope.cn/collections/Qw…

Android control with DGX Spark & Qwen3.5-27B with a simple web UI - Sped up about 4x.

Big moment for text-to-speech. Qwen open-sourced a TTS model that lets you clone voices, design new ones & control speech using natural language. You can ask it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed!

Qwen 3.5 35B is now available on Venice. Fast, efficient, and multilingual. 262K context window, multimodal input, and strong coding and agent performance — all fully private on Venice.

🎯More Qwen3.5 INT4 models are also available now! huggingface.co/Intel/Qwen3.5… huggingface.co/Intel/Qwen3.5… huggingface.co/Intel/Qwen3.5… @Alibaba_Qwen @JustinLin610

Top 10 Open Models: February 2026 in Text Arena. The top 3 labs have not changed since January, but the scores have gotten tighter between them: - @Zai_org's GLM-5, scoring 1455 - @Alibaba_Qwen's Qwen-3.5 397B A17B, scoring1454 - @Kimi_Moonshot's Kimi-K2.5 Thinking, 1452 The spread widens from there. The open leaderboard remains tightly clustered at the top, single-digit swings can reshuffle the overall rankings. See thread for more details on shifts this month.

RIP Monthly Fees. Really! Awesome combo Qwen3.5-35B + openclaw. Run it once, use it all day: $0 per prompt You don’t need huge/expensive models for your daily tasks: - Email +writing - Summaries - Non-stop coding - Agentic automation - Screenshots/images - Multilingual - No limits (except speed and electricity bill) Yeah, not super fast, but works well on 16GB VRAM + 64GB RAM.

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.