Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Create a 16:9 cinematic split-screen poster. LEFT SIDE (40% width): – A simple warehouse-style worktable with neatly stacked plain cardboard boxes, paper tape, a barcode scanner, and a clipboard with shipping notes, suggesting global e-commerce without any logos. – The background is a turquoise / teal abstract field made of stylized blue rods or data fibers, hinting at AI-powered logistics behind the scene. – Use natural or soft cinematic lighting. No glowing effects, no neon, no holographic UI. RIGHT SIDE (60% width): – A green-toned abstract aerial forest canopy texture representing growth and reach. – On top of the forest texture, place two clean rounded rectangles stacked vertically near the center-right. – The TOP rectangle contains the text: “Alibaba”. – The BOTTOM rectangle contains the text: “2025/10/10”. – Use a clean sans-serif font with dark green or charcoal text. OVERALL STYLE: – Calm, ordered, and practical, with a hopeful, human-centric mood. – No logos, trademarks, or extra wording beyond the specified text. – Maintain the sharp turquoise/forest split-screen layout.
There’s still a way to go for omni models to match human-level responsiveness and reasoning—but we won’t stop improving. Hope you love Qwen3-Omni – natively end-to-end multilingual omni model.”” / X https://x.com/Alibaba_Qwen/status/1976267690785505440
🚀 Qwen3-VL-30B-A3B-Instruct & Thinking are here! Smaller size, same powerhouse performance 💪—packed with all the capabilities of Qwen3-VL! 🔧 With just 3B active params, it’s rivaling GPT-5-Mini & Claude4-Sonnet — and often beating them across STEM, VQA, OCR, Video, Agent https://x.com/Alibaba_Qwen/status/1974289216113947039
LFM2-8B-A1B just dropped on @huggingface! 8.3B params with only 1.5B active/token 🚀 > Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B > MoE designed to run on phones/laptops (llama.cpp / vLLM) > Pre-trained on 12T tokens → strong math/code/IF https://x.com/maximelabonne/status/1975561460798628199
Qwen Image Edit 2509 is the new leading open weights image editing model, ranking #3 overall in the Artificial Analysis Image Editing Arena and introducing multi-image editing capabilities! The latest release from Alibaba Qwen trails only Gemini 2.5 Flash (Nano-Banana) and https://x.com/ArtificialAnlys/status/1975993986314813889
Researchers introduced GAIN-RL, a method that fine-tunes language models by training on the most useful examples first. It ranks data using a simple internal signal from the model. On Qwen 2.5 and Llama 3.2, this method matched baseline accuracy in 70 to 80 epochs instead of https://x.com/DeepLearningAI/status/1974640684528243151
Introducing Qwen3-VL Cookbooks! 🧑🍳 A curated collection of notebooks showcasing the power of Qwen3-VL—via both local deployment and API—across diverse multimodal use cases: ✅ Thinking with Images ✅ Computer-Use Agent ✅ Multimodal Coding ✅ Omni Recognition ✅ Advanced https://x.com/Alibaba_Qwen/status/1976479304814145877
🚀 Day 0 Support — Qwen3-VL-30B-A3B-Instruct on NexaSDK We’re excited to announce Day 0 support for Qwen3-VL-30B-A3B-Instruct, a breakthrough in multimodal intelligence, now running natively on NexaSDK. We’ve added full support for the MLX Engine on @Apple Silicon GPUs, https://x.com/nexa_ai/status/1974562612164886659
4/5 The same efficiency gains apply on mobile. Running at 16K context lengths on an iPhone 16 Pro, Jamba outputs nearly 16 tokens/second, outpacing token outputs from Llama 3.2 3B, Qwen 3 1.7B, and Phi-4 Mini. Jamba is the only one that can handle up to 64K.”” / X https://x.com/AI21Labs/status/1975917063278567919
Alibaba has released Qwen3 Omni and Qwen3 Omni Realtime – two natively end-to-end “”omni””-modal models that process text, images, audio, and video in a single unified architecture. Artificial Analysis benchmarking shows competitive Speech to Speech performance, as well as https://x.com/ArtificialAnlys/status/1975904190061834602
Most popular local models in Cline are qwen3-coder & GLM-4.5-Air (guide on how to use them is linked below)”” / X https://x.com/cline/status/1976101061753700400
Qwen3-VL secured 2nd place in the vision leaderboard and became the first open-source model to rank first in both the pure text and visual leaderboards.”” / X https://x.com/Alibaba_Qwen/status/1975360868092420345
More generally: if all of your experiments are “”RL on math with Qwen””, I’m not interested in any outlandish claims you want to make. Qwen’s base models have been (appropriately) aggressively mid-trained for math for a long time. Stop drawing conclusions purely from this.”” / X https://x.com/lateinteraction/status/1976761442842849598
Qwen3-30B-A3B-Instruct-2507-4bit generation on MLX: 473 tokens per sec on M3 Ultra! 🚀 https://x.com/ivanfioravanti/status/1976153645658898453
Thank you @ArtificialAnlys ! 🙏 Qwen Image Edit 2509 ranks #3 overall and leads all open-weight models — enabling multi-image editing with precise control. Try it now: https://x.com/Alibaba_Qwen/status/1976119224339955803
Intelligence performance: The Qwen3 Omni 30B reasoning variant achieves an Artificial Analysis Intelligence Index score of 40, surpassing similarly-sized models like Qwen3 30B, but still trailing Alibaba’s flagship LLM, Qwen3 235B 2507, which scored 57. The Qwen3 Omni 30B https://x.com/ArtificialAnlys/status/1975904195426537596
Z ai’s updated GLM 4.6 (Reasoning) is one of the most intelligent open weights models, with near DeepSeek V3.1 (Reasoning) and Qwen3 235B 2507 (Reasoning) level intelligence 🧠 Key intelligence benchmarking takeaways: ➤ Reasoning Model Performance: GLM 4.6 (Reasoning) scores 56 https://x.com/ArtificialAnlys/status/1975425594679496979
HF demo: https://x.com/Alibaba_Qwen/status/1974290412602040532
