Qwen-Image-2.1 experiments on Apple Silicon (M5 Max, MLX, bf16) with my custom MFLUX 1600×672 (2.39:1), fixed seed: 10 steps → ~26s 20 steps → ~44s 30 steps → ~61s 40 steps → ~79s 50 steps → ~97s ~1.78s per step + ~8s fixed (load, prompt encode, VAE decode) Peak memory: 31 GB Prompt: A city street bends upward at the horizon: the ground and its buildings curve up from street level and arc into the sky like a giant wave, one single continuous landscape where skyscrapers grow progressively more vertical until they tower overhead, the road itself rising and wrapping up into the clouds. Dream physics like a folded dream city: no mirror duplication, just one seamless city rolling from horizontal to vertical. Wet reflective asphalt, cool blue-grey palette with warm window lights, glass and steel towers, low clouds threading between the rising districts, slight lens distortion, IMAX 70mm film look, cinematic wide-angle, photorealistic, volumetric haze, epic impossible scale.

As a Dev ambassador I got access to a preview of @Alibaba_Qwen image 2.1 Same prompt: first image is Qwen 2.1, Second is @higgsfield Qwen image 2.1s lighting is really impressive!

I got early access to @Alibaba_Qwen's new image model, Qwen-Image 2.1. I ran 5 prompts (one-shot generation) covering a dense infographic, a multi-panel comic, a macro photo, a chalkboard menu, and a movie poster. Thread below 🧵

Meet Qwen3.8-LiveTranslate, Qwen's next-generation real-time simultaneous interpretation model! 📢 Built on an Interleave architecture, it improves faithfulness, fluency, and conciseness while reducing average lagging (LAAL) from 2.8s to 2.3s across 60 languages. New capabilities: 🙌 - Real-time speaker diarization — distinguishes speakers in multi-party speech and preserves each speaker's voice through more stable voice cloning. - Synchronized bilingual display — source and translation on screen together. - Long-context disambiguation — leverages conversation history to clarify names and terminology for consistent translations. Let's try Qwen3.8-LiveTranslate! 🥳 - Blog: - QwenCloud:

🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result. Highlights: 🥳 - Audio-video intelligence that gets things done: jointly reason over what's seen and heard, and orchestrate tools across long workflows to auto-edit vlogs, translate short videos, and turn movies into recaps. - A major leap: approaching Gemini 3.8 Flash in audio-video capabilities; +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench. - 1M-token context with agentic perception: actively explore long videos and locate key moments with higher accuracy, using 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are reduced by about 89% compared with Qwen3.5-Omni-Plus, making long-form audio-video understanding and agentic workflows more affordable than ever. To help you build apps around Omni, we're also open-sourcing Qwen-MM-Plugins and Qwen-Live Harness! 🛠️ We can't wait to see what you build with Qwen3.8-Omni-Flash! 👀 - Blog: - Qwencloud: - Qwen Studio: - API: - Qwen-MM-Plugins: - Qwen-Live Harness: coming soon

🎨 Qwen-Image-2.1 is coming, and we're opening 50 early access spots for experienced creators and developers! 🔗 Apply here: 📮 We'll reach out by email if you're in. 💡 Program requirement: publish at least one original showcase or a hands-on review on your social media by Sep 28 at 23:59 (UTC+8). Your honest take, whether glowing or critical, is exactly what helps us make it better.

Today I had lunch in @Alibaba_Qwen HQ in Hangzhou! With @QwenDevs team we have talked about Qwen, Wan, Qwen Image and ModelScope! So much energy and passion 🚀 Be ready because there is a lot to come! 🤐 It was a pleasure meeting @HaaaaaaydenH in person after interacting on X since January!

【御礼】 QwenMeetupTokyoが無事終了しました！ オープンモデルやModel Studioの話題で熱気に包まれ、200名超の方々と大盛況の一日となりました。 参加者の皆様、サポートいただいたAlibaba Cloudの皆様、本当にありがとうございました！ #QwenMeetupTokyo#

Meet E-Commerce Bench, a new benchmark for long-horizon autonomous business operations. 🚀 Agents start with ¥100,000 to run online stores for 365 days, handling sourcing, negotiation, pricing, promotions, inventory and cash flow, in a market driven by real e-commerce data. What's inside: 👀 - Real economics: 6886 products, 576 suppliers (152 fraudsters), 600-minute workdays, storage fees, returns and reputation. - Long-horizon learning: almost no model learns to buy cheaper or improve its strategies over a full year of operation. - Seven-axis evaluation: beyond year-end assets, we score six more dimensions, revealing that no single model dominates across the board. Learn more about E-Commerce Bench: 👇 - Blog: - Paper: - Project:  - Code: 

On September 1, we’re going live with Qwen Ambassadors for a community show-and-tell featuring projects built with Qwen3.8 models. They’ll walk us through what they built, how they used the models, and what they learned along the way. If you’re building with Qwen3.8 or curious to see what others are working on, join us on Zoom! Register here: