Qwen(@Alibaba_Qwen)

Authentic Details and Deep Knowledge are also major upgrades in this release. Together, they enable ...

Qwen (@Alibaba_Qwen) — Wed, 22 Jul 2026 12:28:12 GMT

Authentic Details and Deep Knowledge are also major upgrades in this release. Together, they enable Qwen-Image-3.0 to make real breakthroughs in high-value productivity scenarios — newspaper PDFs, short-drama storyboards, complex UI, and more. As image generation keeps advancing, we believe it will unlock genuine productivity value across design, content creation, education, e-commerce, and beyond.

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Beyond horizontal expansion, depth is another key trait of Rich Content — testing the model's semant...

Qwen (@Alibaba_Qwen) — Wed, 22 Jul 2026 12:28:11 GMT

Beyond horizontal expansion, depth is another key trait of Rich Content — testing the model's semantic deconstruction and logical nesting: rendering multiple nested interfaces layer by layer within a single image.

The example below uses one instruction to display, from outer to inner: VSCode → Qwen Chat → a messaging app → a pour-over coffee poster. Each layer preserves the authentic style of its UI, forming a "picture-in-picture-in-picture" visual depth.

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🎨 Meet Qwen-Image-3.0 — the third generation of our foundational image generation model. If 1.0 wa...

Qwen (@Alibaba_Qwen) — Wed, 22 Jul 2026 12:28:08 GMT

🎨 Meet Qwen-Image-3.0 — the third generation of our foundational image generation model.

If 1.0 was about "Precision," and 2.0 added "Variety, Completeness, Beauty & Authenticity," then 3.0 comes down to a single word: Real (实).

Three dimensions of "Real":
📰 Rich Content — prompts up to 4.5k tokens. One-pass generation of complex layouts: newspapers, storyboards, exam papers — even a 3×3 infographic grid or picture-in-picture-in-picture UIs.
🔬 Authentic Details — text legible down to 10px, full LaTeX paper pages, pores, hair strands & near-photographic skin texture.
🌏 Deep Knowledge — native rendering in 12 languages, 100+ art styles, realistic UIs (web / games / livestreams), plus world knowledge & live web retrieval.

Not just "good-looking" — genuinely useful. Image generation as a real productivity tool for design, content, education & e-commerce.

Go create 🏃🎨

💬Qwenchat.qwen.ai/?inputFeature=…HmITJ2W
qwen.ai/blog?id=qwen-i…nS4uI9Ar

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Qwen (@Alibaba_Qwen) — Tue, 21 Jul 2026 14:08:17 GMT

🔥🔥

Qwen Cloud@qwen_cloud

Token Plan Individual is now live on Qwen Cloud, starting at just $6/month.🚀

One plan gives you access to the Qwen family, HappyHorse, GLM, and DeepSeek，and you can be among the first to try Qwen3.8-Max-Preview.

Even better, a single Token Plan can power up to 8 agents at the same time (for Pro Plan), so your whole agent stack runs on one subscription. 

And, Harness tools like web search, image search, and code interpreter are included too.

🌍Try On Qwen Cloudqwencloud.com/pricing/token-…IV

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Qwen (@Alibaba_Qwen) — Tue, 21 Jul 2026 13:45:52 GMT

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Qoder@qoder_ai_ide

Day 0 on Qoder: Qwen3.8-Max-Preview is live. 🚀

2.4T params + huge gains in Coding & Cowork — running on Qoder's agentic core, built to run autonomous, long-horizon tasks end to end while you step away.

Up to 98% off right now (0.5x → 0.01x). The window to go big is open.

Start atqoder.comV

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@israfill @synthwavedd Qwen Studio- Web Dev: https://t.co/k0hPTzegqy

Qwen (@Alibaba_Qwen) — Mon, 20 Jul 2026 12:16:10 GMT

@israfill@synthwavedd Qwen Studio- Web Dev:  chat.qwen.ai/?inputFeature=…

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@Haleeeemahh Token Plan, Qoder, and QoderWork. Token Plan： international：https://t.co/9G5yUsQxuM ...

Qwen (@Alibaba_Qwen) — Mon, 20 Jul 2026 12:11:18 GMT

@Haleeeemahh Token Plan, Qoder, and QoderWork.  

Token Plan：
international：qwencloud.com/pricing/token-…
China：platform.qianwenai.com/pricing/token-…

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During Preview, Qwen3.8 is getting better by the day. Latest version is live now, with broad gains a...

Qwen (@Alibaba_Qwen) — Mon, 20 Jul 2026 11:53:00 GMT

During Preview, Qwen3.8 is getting better by the day. Latest version is live now, with broad gains and a big step up on web frontend.

Thank you all — the response to Qwen3.8-Max-Preview blew us away. 🫶🫶

Qwen3.8 is still evolving daily. Come test it, and tell us what breaks.

We're looking forward to a more capable, official version — and to open-weight it for everyone.🚀🚀

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Qwen3.8 is launching and going open-weight soon!🌐 With a massive 2.4T parameters, this model is co...

Qwen (@Alibaba_Qwen) — Sun, 19 Jul 2026 08:29:31 GMT

Qwen3.8 is launching and going open-weight soon!🌐

With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5.

You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very first to try it out.

Can't wait to hear what you build. Stay tuned! 🚀 

Token Plan 
internationaqwencloud.com/pricing/token-…Bv
Chinplatform.qianwenai.com/pricing/token-…Rp

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Qwen3.8 is launching and going open-weight soon!🌐 With a massive 2.4T parameters, this model is co...

Qwen (@Alibaba_Qwen) — Sun, 19 Jul 2026 08:10:39 GMT

Qwen3.8 is launching and going open-weight soon!🌐

With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5.

You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very first to try it out.

Can't wait to hear what you build. Stay tuned! 🚀 

Token Plan 
internationaqwencloud.com/pricing/token-…Bv
Chinplatform.qianwenai.com/pricing/token-…Rp

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We open-source Qwen-AgentWorld-35B-A3B (MoE, 35B/3B active, 256K context) and AgentWorldBench. Two ...

Qwen (@Alibaba_Qwen) — Wed, 24 Jun 2026 09:52:54 GMT

We open-source Qwen-AgentWorld-35B-A3B (MoE, 35B/3B active, 256K context) and AgentWorldBench.

Two routes, one roadmap:
🔬 Build the simulator — scalable, controllable, surpassing real environments
🧠 Internalize world modeling — predict before you act

Qwen-AgentWorld is our attempt to investigate how language world modeling can further expand the boundaries of general agent capabilities.

Go build on it 🏃🏃‍♂️

📑 Paarxiv.org/abs/2606.24597RKq71
📖 qwen.ai/blog?id=qwen-a…Kyhsx2
💻 Ggithub.com/QwenLM/Qwen-Ag…vb1UZCn
🤗 Huggihuggingface.co/collections/Qw…3QBL1TM5
🧩 Modmodelscope.cn/collections/Qw…BnGYgMWWI

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🧠 Paradigm II — Agent Foundation Model: world modeling as agent capability. Single-turn, non-agent...

Qwen (@Alibaba_Qwen) — Wed, 24 Jun 2026 09:52:51 GMT

🧠 Paradigm II — Agent Foundation Model: world modeling as agent capability.

Single-turn, non-agentic environment prediction → tested directly on multi-turn, tool-calling agent tasks. No agentic RL, no task-specific tuning.

Gains across 7 benchmarks, including 3 entirely out-of-domain:
- In-domain: Terminal-Bench 2.0 +6.3, SWE-Bench +3.4, WideSearch +12.8
- Out-of-domain: Claw-Eval +11.3, QwenClawBench +9.7, BFCL v4 +9.0

World modeling internalizes "predict before you act" as a transferable reasoning pattern.

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📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP,...

Qwen (@Alibaba_Qwen) — Wed, 24 Jun 2026 09:52:43 GMT

📣📣 Meet  Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation.

🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves.

🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes:

1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench

2️⃣ Investigate how world modeling enhances agent training:
🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments
🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning

📑 arxiv.org/abs/2606.24597l5RKq71
�qwen.ai/blog?id=qwen-a…VcKyhsx2
💻github.com/QwenLM/Qwen-Ag…5Lvb1UZCn
🤗 Hughuggingface.co/collections/Qw…Kw3QBL1TM5
🧩 Mmodelscope.cn/collections/Qw…/YBnGYgMWWI

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Qwen-Robot Suite，enable AI from chatbot to physical action in the real world. More demos, please vis...

Qwen (@Alibaba_Qwen) — Tue, 16 Jun 2026 13:07:14 GMT

Qwen-Robot Suite，enable AI from chatbot to physical action in the real world.
More demos, please visit our blog:qwen.ai/blog?id=qwen-r…

Your browser does not support the video tag.

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By treating natural language as a universal action interface，Qwen-RobotWorld bridges the gap betwee...

Qwen (@Alibaba_Qwen) — Tue, 16 Jun 2026 13:07:13 GMT

By treating natural language as a universal action interface，Qwen-RobotWorld bridges the gap  between general video generation models and domain-specific embodied models — this converts end-effector poses, steering commands, and navigation waypoints into a single interface, enabling 20+ embodiment types and 500+ action categories to be co-trained under the Embodied World Knowledge corpus (8.6M video-text pairs, 200M+ frames), with each domain's physical knowledge reinforcing the others.
Qwen-RobotWorld performs strongly across EWMBench/DreamGen/WorldModelBench/PBench benchmarks.

Blog Link：qwen.ai/blog?id=qwen-r…

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📣 Introducing the Qwen-Robot Suite — Qwen-RobotNav, Qwen-RobotManip, Qwen-RobotWorld, three foundat...

Qwen (@Alibaba_Qwen) — Tue, 16 Jun 2026 13:07:10 GMT

📣 Introducing the Qwen-Robot Suite — Qwen-RobotNav, Qwen-RobotManip, Qwen-RobotWorld, three foundation models, a full stack for embodied intelligence.

🧭 Qwen-RobotNav — the gateway to mobility.
• Unifies 5 navigation tasks in one model: instruction following, point-goal, object-goal, target tracking, autonomous driving
• Controllable observation protocol
• Tool interface for agentic systems

🤖 Qwen-RobotManip —  the foundation of interaction.
• Unified state-action space across heterogeneous robots
• Camera-frame delta poses for coherent cross-embodiment training
• Pretrained on a 38,100+ hour open-source corpus

🌍 Qwen-RobotWorld — infinite worlds for physical agents.
• Single world model, 20+ embodiments
• Natural-language action interface
• Predicts physically grounded futures across manipulation, driving, and navigation

Each model is independently useful, and could be composed as physical-world tools.Together, they form the low-level toolkit for general-purpose agentic systems that don't just see the world, but act in it.

📷 Bqwen.ai/blog?id=qwen-r…YET26
📖 Report：
Qwen-Robo…anwen-res.oss-accelerate.aliyuncs.com/qwenrobot/pape…wDYGxg
Qwen-RobotM…anwen-res.oss-accelerate.aliyuncs.com/qwenrobot/pape…zJSpU8
Qwen-RobotW…anwen-res.oss-accelerate.aliyuncs.com/qwenrobot/pape…1qzDFy

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Demo3：Browser Agent

Qwen (@Alibaba_Qwen) — Mon, 01 Jun 2026 17:55:46 GMT

Demo3：Browser Agent

Your browser does not support the video tag.

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Demo2: Multimodal Interactive Hybrid Agent

Qwen (@Alibaba_Qwen) — Mon, 01 Jun 2026 17:55:01 GMT

Demo2: Multimodal Interactive Hybrid Agent

Your browser does not support the video tag.

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Performance1：Text Benchmarks Qwen3.7-Plus delivers competitive text performance that approaches Max...

Qwen (@Alibaba_Qwen) — Mon, 01 Jun 2026 17:54:19 GMT

Performance1：Text Benchmarks

Qwen3.7-Plus delivers competitive text performance that approaches Max-tier models across the board.

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👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one v...

Qwen (@Alibaba_Qwen) — Mon, 01 Jun 2026 17:54:18 GMT

👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one versatile agent foundation.

✅ Multimodal interactive hybrid agent: unified GUI & CLI operation across visual and text tasks
✅ Versatile coding agent & productivity assistant with full-modality input
✅ Visual Agent: perception, reasoning, grounding, and search-augmented QA
✅ Cross-harness generalization across diverse agent frameworks

One model. Sees, thinks, codes, acts.🙌🙌

Now available via API on Alibaba Cloud Model Studio. Try it — let us know what you build.😎

🔗🔗⬇️⬇qwen.ai/blog?id=qwen3.…f0h3NNa
Qwen chat.qwen.ai/?models=qwen3.…gFW4cmodelstudio.console.alibabacloud.com/ap-southeast-1…0cXrMzW

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📢Qwen3.7-Max just hit #3 on ITbench-AA — a fresh benchmark testing how well models handle real-worl...

Qwen (@Alibaba_Qwen) — Thu, 28 May 2026 06:55:00 GMT

📢Qwen3.7-Max just hit #3 on ITbench-AA — a fresh benchmark testing how well models handle real-world enterprise IT tasks, agentic-style.

🔧Agentic era, go with Qwen.🏃🏃

Artificial Analysis@ArtificialAnlys

Artificial Analysis and IBM Research are launching ITBench-AA, the first in a new series of benchmarks evaluating models on agentic enterprise IT tasks, starting with Site Reliability Engineering tasks where frontier models score below 50%

ITBench-AA’s SRE tasks benchmark model performance on Kubernetes incident response, where models must diagnose live systems by reading logs, tracing dependencies, and identifying root-cause entities across complex infrastructure. The underlying ITBench dataset has been developed by @IBM's Software Innovation Lab, leveraging IBM’s deep expertise in enterprise IT operations

Artificial Analysis has worked closely with IBM over the last 6 months to develop a implementation of the dataset for frontier AI evaluation, beginning with Site Reliability Engineering (SRE) and expanding to Financial Operations (FinOps) and Chief Information Security Officer (CISO) tasks over time

ITBench-AA SRE overview:
➤ 59 SRE tasks in total: 40 public tasks and 19 brand new, held-out tasks
➤ Each task provides a Kubernetes incident snapshot containing alerts, events, traces, metrics, logs, and application topology. The model must identify the minimal set of independent root-cause Kubernetes entities responsible for the incident
➤ Faults span typical SRE failure modes including infrastructure, service, application, and chaos-injected incidents, such as resource quota exhaustion, rollout failures, connection pool exhaustion, and network partitions

Methodology details:
➤ Agentic harness: each task is solved by the model running in our open-source Stirrup reference harness, with shell access to a sandboxed file system containing the relevant logs and snapshots. 100-turn cap per task, 3 repeats per task
➤ Models submit a list of root-cause entities (Kubernetes Deployments, Services, Pods, etc.) they believe caused the incident. Each submission is compared against a ground-truth set of root causes provided by IBM Research
➤ Scoring uses average precision at full recall: if a model misses any of the ground-truth root causes, it scores 0.0 for that repeat. If it identifies all of them, it is awarded a score equal to its precision - the share of its submitted entities that are actual root causes, i.e. true positives / (true positives + false positives). The headline score is the average across 59 tasks × 3 repeats.
➤ The harness (Stirrup) is held constant across all evaluated models, allowing an apples-to-apples comparison between models.

Key findings:
➤ Claude Opus 4.7 (Adaptive Reasoning, Max Effort) leads at 47%, followed by GPT-5.5 (xhigh) at 46% and Qwen3.7 Max at 42%
➤ All frontier models score below 50%, making ITBench-AA SRE one of the least saturated agentic benchmarks in our suite. For context, frontier models score considerably higher on Terminal-Bench
➤ Turn counts vary nearly 3x and longer trajectories do not translate to higher accuracy. GPT-5.5 (xhigh) averages 31 turns per task at 46%, while Gemini 3.1 Pro Preview averages 83 turns at 30%. Models that over-investigate tend to surface upstream fault-injection mechanisms or co-occurring symptoms as false positives
➤ GLM-5.1 (Reasoning) leads open weights models at 40%, effectively tied with Gemini 3.5 Flash (high). DeepSeek V4 Pro (Reasoning, Max Effort) follows at 38%, with Gemma 4 31B (Reasoning) at 37%, ahead of Gemini 3.1 Pro Preview at 30%

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Fast, faster, Qwen. 🚀 Thrilled to see Qwen3.5 reaching a record-breaking 580 tps for agentic workl...

Qwen (@Alibaba_Qwen) — Wed, 27 May 2026 16:34:20 GMT

Fast, faster, Qwen. 🚀

Thrilled to see Qwen3.5 reaching a record-breaking 580 tps for agentic workloads on the TokenSpeed engine! This milestone wouldn't be possible without our incredible partners.

Huge thanks to @lightseekorg, @NVIDIAAI, the Mooncake team, and @tri_dao for the pioneering FA4 optimization. Together, we are pushing the boundaries of open-source LLM inference. 🤝✨

Dive into the full @PyTorch blog post below! pytorch.org/blog/up-to-580…cZ#Qwenw#Qwen3_53#TokenSpeede#LLML#Inferencen#AI##PyTorchr#OpenSourcer#AgenticAIc#HighPerformancence

PyTorch@PyTorch

The speed-of-light optimization for Qwen3.5 on the TokenSpeed inference engine is a significant milestone, achieving a record-breaking 580 tokens per second (tps) for agentic workloads on NVIDIA GPUs.

In the PyTorch Foundation's latest community blog post, you can learn all about the complete design, implementation, and optimization of Qwen3.5 models in the TokenSpeed inference framework and see for yourself how this work is improving performance 👉bit.ly/4uGUvISk

This achievement was a joint effort between the @Alibaba_Qwen  inference team, @lightseekorg  Foundation TokenSpeed team, @NVIDIAAI , and the Mooncake team, with special contributions from @tri_dao  for FlashAttention-4 (FA4) optimization. @KVCache_AI

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🚀🚀

Qwen (@Alibaba_Qwen) — Wed, 27 May 2026 12:24:48 GMT

🚀🚀

Nous Research@NousResearch

Qwen 3.7 Max is now supported in Hermes Agent

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🙌🙌

Qwen (@Alibaba_Qwen) — Wed, 27 May 2026 10:43:51 GMT

🙌🙌

Qoder@qoder_ai_ide

Qwen3.7-Max, halved. 

Starting today, Qwen3.7-Max — the latest flagship from the @Alibaba_Qwen family — is half price on Qoder. Limited time. 

New here? You also get 100 free model calls a day. Auto-applied. No claim. No toggle. 
Desktop, JetBrains Plugin, CLI, QoderWork, QoderWake — all covered. 

Now's a good time to put it on something hard.

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🚀🚀 Qwen3.7-Max just hit #4 on Code Arena, on par with Claude Opus 4.6 ，top-ranked Chinese lab on t...

Qwen (@Alibaba_Qwen) — Wed, 27 May 2026 01:23:27 GMT

🚀🚀 Qwen3.7-Max just hit #4 on Code Arena, on par with Claude Opus 4.6 ，top-ranked Chinese lab on the board!  @arena 

More to ship. Stay tuned. 🕶️

Arena.ai@arena

Qwen3.7 Max (20250517) debuts at #4 in Code Arena: Frontend - the top-ranked Chinese lab on the board, surpassing GLM-5.1 and is now on par with Claude Opus 4.6 on agentic web development tasks.

Huge congrats to @Alibaba_Qwen on this achievement!

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✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. ⚡️Faster + ...

Qwen (@Alibaba_Qwen) — Mon, 25 May 2026 15:26:13 GMT

✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. 
⚡️Faster + cheaper out of the box.

Need higher, more deterministic hit rates? Try explicit caching instead. 🙌
🔗Best practices �alibabacloud.com/help/en/model-…uBH

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⚡️⚡️

Qwen (@Alibaba_Qwen) — Fri, 22 May 2026 10:16:42 GMT

⚡️⚡️

OpenRouter@OpenRouter

The new Qwen3.7-Max from @Alibaba_Qwen is live on OpenRouter.

The flagship of the Qwen3.7 series, built for agent-centric work: coding, office and productivity tasks, and long-horizon autonomous execution. Big jumps in coding and agent benchmarks over Qwen3.6, with explicit prompt caching for repeated context.

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Qwen (@Alibaba_Qwen) — Fri, 22 May 2026 10:04:52 GMT

👀👀

atomic.chat@atomic_chat_hq

Qwen 3.7-max beats Opus 4.7 and GPT-5.5

We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head.

Qwen 3.7-Max: training cost $1.32, bot improvement +56%
Claude Opus 4.7: training cost $12.15, bot improvement +28%
GPT-5.5: training cost $2.85, bot improvement +7%

Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

Your browser does not support the video tag.

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🚀Qwen3.7-Max just landed at 56.6 on the Artificial Analysis Intelligence Index — a solid 4.8pt jump...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:20:35 GMT

🚀Qwen3.7-Max just landed at 56.6 on the Artificial Analysis Intelligence Index — a solid 4.8pt jump over Qwen3.6-Max-Preview. @ArtificialAnlys 

⚡️Sharper sci reasoning, stronger agentic chops, better coding, and it hallucinates less.

Artificial Analysis@ArtificialAnlys

Alibaba’s new Qwen3.7 Max model scores 56.6 on the Artificial Analysis Intelligence Index, 4.8 points higher than Qwen3.6 Max Preview (51.8). While Alibaba still trails models from OpenAI, Anthropic and Google, Qwen3.7 Max is the closest they have been to the frontier

Qwen3.7 Max is @Alibaba_Qwen's latest proprietary flagship, scoring 56.6 on the Intelligence Index, a 4.8 point gain over Qwen3.6 Max Preview (51.8) released in April. Qwen3.7 Max continues Alibaba's pattern, in place since Qwen2.5 Max (January 2025), of releasing Max and Plus models as closed weights while the rest of the Qwen line remains open weights. The leading open weights Qwen on the Intelligence Index is Qwen3.6 27B (Reasoning, 45.8) released in April 2026, and the leading open weights MoE Qwen is Qwen3.5 397B A17B (Reasoning, 45.0) released in February 2026

Key takeaways for the reasoning variant:
➤ The Intelligence Index gains over Qwen3.6 Max Preview are concentrated in scientific reasoning, agentic capability and coding. CritPt +9.7 p.p (3.7% to 13.4%), HLE +9.2 p.p (28.9% to 38.1%), TerminalBench Hard +6.9 p.p (43.9% to 50.8%) and GDPval-AA +42 Elo (1504 to 1546). Scores on other benchmarks in the Intelligence Index are flat compared to Qwen3.6 Max Preview

➤ A significant share of the Intelligence Index gain is driven by higher abstention on AA-Omniscience, not higher accuracy. Qwen3.7 Max's accuracy on AA-Omniscience dropped 7.6 p.p (37.7% to 30.1%), while its hallucination rate dropped 21.3 p.p (44.2% to 22.9%). The model is choosing not to answer more questions rather than recalling more facts. Because hallucination rate and accuracy both feed into the Intelligence Index, the hallucination reduction is one of the larger single contributors to the +4.8 point gain on the Intelligence Index

➤ Qwen3.7 Max used 96.7M output tokens to run the Intelligence Index, ~31% more than Qwen3.6 Max Preview (73.9M). It sits mid-pack on frontier token usage: above GPT-5.5 (high, 44.5M) and Gemini 3.1 Pro Preview (57.3M), below Claude Opus 4.7 (Adaptive Reasoning, Max Effort, 112M), Kimi K2.6 (166M) and DeepSeek V4 Pro (Reasoning, Max Effort, 187M)

Key model details:
➤ Context window: 1M tokens (up from 256K on Qwen3.6 Max Preview) 
➤ Multimodality: Text input and output only 
➤ Pricing: Yet to be announced (Qwen3.6 Max Preview is priced at $1.30/$7.80 per 1M input/output tokens on the @alibaba_cloud first-party API) 
➤ Licensing: Proprietary, closed weights

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Cowork Productivity Assistant：Qwen3.7-Max serves as your advanced coworker for real-world productivi...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:37 GMT

Cowork Productivity Assistant：Qwen3.7-Max serves as your advanced coworker for real-world productivity.

Your browser does not support the video tag.

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Self-Evolving in the Wild：Over the course of ~35 hours of continuous autonomous execution, the model...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:36 GMT

Self-Evolving in the Wild：Over the course of ~35 hours of continuous autonomous execution, the model performed 432 kernel evaluations across 1,158 tool calls. It wrote, compiled, profiled, and iteratively improved the Extend Attention Kernel entirely on its own — 10.0x geometric mean speedup over the Triton reference, measured across multiple workloads.

More details on blog:qwen.ai/blog?id=qwen3.7

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Cross-Harness Generalization：Across QwenClawBench and CoWorkBench, Qwen3.7-Max delivers strong, cons...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:35 GMT

Cross-Harness Generalization：Across QwenClawBench and CoWorkBench, Qwen3.7-Max delivers strong, consistent performance regardless of the harness used at evaluation time, confirming that the model has learned to solve tasks — not to exploit particular harnesses.

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Agent Scaling：Building on Qwen3.5's environment scaling approach, we've aggressively expanded the qu...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:34 GMT

Agent Scaling：Building on Qwen3.5's environment scaling approach, we've aggressively expanded the quality and diversity of agentic training environments in Qwen3.7 — agentic capabilities generalize from diverse environments, just as language models do from diverse text. The figure below shows a clear and consistent improvement trajectory, with Qwen3.7-Max achieving a top-3 average ranking that approaches Claude-4.6-Opus-Max.

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Performance：Qwen3.7-Max performs strongly across benchmarks in coding agents , and improves massivel...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:33 GMT

Performance：Qwen3.7-Max performs strongly across benchmarks in coding agents , and improves massively in general-purpose agents. Qwen3.7-Max also demonstrates exceptional strength on the hardest reasoning benchmarks, and stands out in general capabilities and multilingualism.

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📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents...

Qwen (@Alibaba_Qwen) — Thu, 21 May 2026 13:15:32 GMT

📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era.

A versatile foundation for agents that actually get things done:
🧑‍💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it.
🗂️ A reliable office and productivity assistant. Get your work done through MCP integrations and multi-agent orchestration.
⏱️ Long-horizon autonomy.  35 hours straight on a kernel optimization task — 1,000+ tool calls, zero hand-holding.
🔌 Scaffold-agnostic. Claude Code, OpenClaw, Qwen Code, or your own stack. Consistent reliability everywhere.

API's up on Alibaba Model Studio. You can also take it for a spin on Qwen Studio.

Go build something wild!🏃🏃‍♂️

�qwen.ai/blog?id=qwen3.7AupX3Pa0
✅ Qwen chat.qwen.ai/?models=qwen3.…TnrCBjWtmodelstudio.console.alibabacloud.com/ap-southeast-1…ys00osKn

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🚀🚀

Qwen (@Alibaba_Qwen) — Mon, 18 May 2026 15:58:37 GMT

🚀🚀

Arena.ai@arena

In the Vision Arena, Qwen3.7 Plus Preview makes @Alibaba_Qwen the #5 lab, ranking #16 overall.

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🚀🚀Qwen3.7 Preview lands on Arena ！ Here come Qwen3.7-Max-Preview & Qwen3.7-Plus-Preview. Ali...

Qwen (@Alibaba_Qwen) — Mon, 18 May 2026 15:56:36 GMT

🚀🚀Qwen3.7 Preview lands on Arena ！

Here come Qwen3.7-Max-Preview & Qwen3.7-Plus-Preview.  Alibaba now #6 lab in Text, #5 in Vision.⚡️⚡️

Can't wait to release Qwen3.7 series models！Stay tuned! @arena

Arena.ai@arena

Qwen3.7 Preview By @Alibaba_Qwen lands on Arena for Text and Vision.

In Text Arena, Qwen3.7 Max Preview ranks #13 overall.
Alibaba is now the #6 lab in this arena.
- #7 Math
- #9 Expert
- #9 Software & IT
- #10 Coding

In Vision Arena: Qwen3.7 Plus Preview ranks #16 overall, making Alibaba the #5 lab.

Congrats to the @Alibaba_Qwen team on the latest progress!

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🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time. Hermes Agent, here we go!! ⚡️ @Nou...

Qwen (@Alibaba_Qwen) — Wed, 13 May 2026 03:05:35 GMT

🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time.
Hermes Agent, here we go!! ⚡️ @NousResearch

Nous Research@NousResearch

Qwen 3.6 Plus by @Alibaba_Qwen is now FREE for a limited time on Nous Portal!

Nous Portal is one easy subscription that gives you access to 300+ models, exclusive discounts, and bundles your tokens and paid tools together for hassle-free setup and simple billing.

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📣We're calling for ambassadors! Whether you're a developer with great technical taste or a local c...

Qwen (@Alibaba_Qwen) — Mon, 11 May 2026 13:51:00 GMT

📣We're calling for ambassadors!

Whether you're a developer with great technical taste or a local community leader who loves bringing people together, we'd love to have you join us.

Visit the website below for more details and to apply. In return, ambassadors will receive early access to Qwen models, API credits, annual merchandise, and more.

Come and check it out!�qwen.ai/ambassadorUg

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📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models...

Qwen (@Alibaba_Qwen) — Fri, 01 May 2026 15:14:01 GMT

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models 

We are pleased to announce a strategic partnership between Qwen and Fireworks AI to deliver optimized, production-ready deployment of Qwen's closed weights models via the Fireworks Platform. @FireworksAI_HQ 

This collaboration empowers developers and enterprises to:
✅ Deploy Qwen models with lower latency and reduced fine tuning and inference costs
✅ Leverage enterprise-grade reliability, security, and scalability
✅ Integrate seamlessly into modern AI workflows

🔹 Get started with Qwen on Fireworksapp.fireworks.ai/models/firewor…M4#Qwenen#FireworksAIAI#OpenSourceAIAI#LLMLM#AIInfrastructurere#ResponsibleAIAI#DeveloperCommunityty

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Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family....

Qwen (@Alibaba_Qwen) — Thu, 30 Apr 2026 14:39:16 GMT

Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family. It turns SAE features into practical tools：

🎯 Inference — Steer model outputs by directly manipulating internal features, no prompt engineering needed
📂 Data — Classify & synthesize targeted data with minimal seed examples, boosting long-tail capabilities
🏋️ Training — Trace code-switching & repetitive generation back to their source, fix them at the root
📊 Evaluation — Analyze feature activation patterns to select smarter benchmarks and cut redundancy

We hope the community uses Qwen-Scope to uncover new mechanisms inside Qwen models and build applications beyond what we explored.Excited to see what you build! 🚀

🔗�qwen.ai/blog?id=qwen-s…wiE1tnb9
Huggihuggingface.co/collections/Qw…ICpK8eXG
Modemodelscope.cn/collections/Qw…v1FjmPaW
Technical …anwen-res.oss-accelerate.aliyuncs.com/qwen-scope/Qwe…MjEZK0sa

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Forward and backward benchmark results across common configurations.

Qwen (@Alibaba_Qwen) — Wed, 29 Apr 2026 12:16:17 GMT

Forward and backward benchmark results across common configurations.

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🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar...

Qwen (@Alibaba_Qwen) — Wed, 29 Apr 2026 12:16:13 GMT

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang.

⚡ 2–3× forward speedup. 2× backward speedup.
💻 Purpose-built for agentic AI on your personal devices.

💡Key insights:
1. Gate-driven automatic intra-card CP.
2. Hardware-friendly algebraic reformulation.
3. TileLang fused warp-specialized kernels.

FlashQLA boosts SM utilization via automatic intra-device CP. The gains are especially pronounced for TP setups, small models, and long-context workloads.

Instead of fusing the entire GDN flow into a single kernel, we split it into two kernels optimized for CP and backward efficiency. At large batch sizes this incurs extra memory I/O overhead vs. a fully fused approach, but it delivers better real-world performance on edge devices and long-context workloads.

The backward pass was the hardest part: we built a 16-stage warp-specialized pipeline under extremely tight on-chip memory constraints, ultimately achieving 2×+ kernel-level speedups. 

We hope this is useful to the community!🫶🫶
Learn more:
📖 qwen.ai/blog?id=flashq…piR4yf
💻github.com/QwenLM/FlashQLAaf5L1AZ

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🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar...

Qwen (@Alibaba_Qwen) — Wed, 29 Apr 2026 12:15:51 GMT

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang.

⚡ 2–3× forward speedup. 2× backward speedup.
💻 Purpose-built for agentic AI on your personal devices.

💡Key insights:
1. Gate-driven automatic intra-card CP.
2. Hardware-friendly algebraic reformulation.
3. TileLang fused warp-specialized kernels.

FlashQLA boosts SM utilization via automatic intra-device CP. The gains are especially pronounced for TP setups, small models, and long-context workloads.

Instead of fusing the entire GDN flow into a single kernel, we split it into two kernels optimized for CP and backward efficiency. At large batch sizes this incurs extra memory I/O overhead vs. a fully fused approach, but it delivers better real-world performance on edge devices and long-context workloads.

The backward pass was the hardest part: we built a 16-stage warp-specialized pipeline under extremely tight on-chip memory constraints, ultimately achieving 2×+ kernel-level speedups. 

We hope this is useful to the community!🫶🫶
Learn more:
📖 qwen.ai/blog?id=flashq…piR4yf
💻github.com/QwenLM/FlashQLAaf5L1AZ

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Balanced Performance Across Artistic Styles： More uniform quality across diverse aesthetic domains, ...

Qwen (@Alibaba_Qwen) — Sat, 25 Apr 2026 13:02:52 GMT

Balanced Performance Across Artistic Styles：
More uniform quality across diverse aesthetic domains, effectively reducing style-dependent quality variance.🏖️

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Comprehensive Multilingual Text Rendering： Better glyph accuracy, more consistent typography, and cl...

Qwen (@Alibaba_Qwen) — Sat, 25 Apr 2026 13:02:51 GMT

Comprehensive Multilingual Text Rendering：
Better glyph accuracy, more consistent typography, and cleaner layouts even in complex compositions.
It handles mixed-language scenarios more gracefully as well.
If you’re building posters, UI mockups, ads, or anything text-heavy, this should feel like a meaningful step forward.🪄

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Qwen-Image-2.0-Pro is now live 🚀🚀 We’ve pushed image quality, multilingual text rendering, and in...

Qwen (@Alibaba_Qwen) — Sat, 25 Apr 2026 12:54:04 GMT

Qwen-Image-2.0-Pro is now live 🚀🚀

We’ve pushed image quality, multilingual text rendering, and instruction following to a new level, while making performance much more consistent across styles.🌅🌃

Ranked #9 worldwide for Text-to-Image on @arena 

🔗Try it now on 
ModelScmodelscope.ai/studios/Qwen/Q…jmodelscope.cn/studios/Qwen/Q…WMEMPmodelstudio.console.alibabacloud.com/ap-southeast-1…qt2bF

Arena.ai@arena

Qwen Image 2.0 Pro 2026-04-22 lands at #9 in Text-to-Image Arena.

Highlights of the latest image model from @Alibaba_Qwen:
- #9 Text-to-Image
- #17 Image Edit (Single Image)

Top 10 in Text-to-Image categories:
- #6 Portraits
- #7 Photorealistic & Cinematic Imagery
- #7 Art

Congrats to the @Alibaba_Qwen team on this launch!

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👀👀

Qwen (@Alibaba_Qwen) — Thu, 23 Apr 2026 06:32:27 GMT

👀👀

David Hendrickson@TeksEdge

🚨 OMG! Qwen just released Qwen3.6-27B! This is an update to the King 👑 of Open Source Med Models!

A 27B dense model that beats the much larger Qwen3.5-397B-A17B (~15x bigger) on coding benchmarks! 🤯

Watch out Opus 4.7

Key wins:
• SWE-Bench Verified: 77.2 (vs 76.2) 
• SWE-Bench Pro: 53.5 (vs 50.9) 
• Terminal-Bench 2.0: 59.3 (vs 52.5)
✅ Strong multimodal reasoning 
✅ Thinking + Non-thinking modes 
✅ Apache 2.0 (fully open)

Smaller model. Flagship performance. 🔥

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🐦🐦‍⬛🐤

Qwen (@Alibaba_Qwen) — Thu, 23 Apr 2026 06:31:10 GMT

🐦🐦‍⬛🐤

Simon Willison@simonw

The new Qwen3.6-27B just gave me definitely the best pelican riding a bicycle I've had from a 16.8GB model file! simonwillison.net/2026/Apr/22/qw…