Alibaba Qwen

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Npt

In 1968 five countries that already had nuclear weapons signed a treaty declaring them too dangerous for anyone else to build. India refused, pointing out the treaty did not say nukes were too dangerous to exist, just too dangerous for new entrants. Anthropic built Mythos, deemed it too powerful for public release, then shipped Fable with the same weights but hidden degradation on frontier AI work. The restriction started the day after they finished building. Non proliferation was never about preventing danger. It was about preserving advantage. Mythos 5 goes unrestricted to Microsoft, Nvidia, Google Cloud, AWS, and about 200 other approved partners. Fable 5 goes to everyone else with silent capability limits on frontier ML development. The biggest paying customers get the full product. Potential competitors get a version that quietly gives worse answers on the work that matters most. Anthropic filed confidentially for its IPO one week before this launch. India had a phrase for this kind of arrangement when it refused the NPT. Discriminatory by design. Jensen Huang called the GPU to nuclear bomb comparison stupid. He is wrong about the analogy but right about the instinct behind it. The NPT worked because nuclear weapons require enrichment facilities, centrifuges, and state level infrastructure. AI does not. Qwen has 942 million downloads. DeepSeek V4 ships under MIT license with full weights matching closed frontier models. The knowledge Anthropic is trying to restrict through hidden degradation is already open and available in competing models. You cannot run a non proliferation regime when the material is free to download. Anthropic Fable 5 silently degrades its own performance when it detects someone building a competing model. No warning, no refusal, just worse answers through hidden prompt tweaks and steering vectors Meanwhile DeepSeek published its full R1 training pipeline, failure modes, RL schedules, everything, under MIT license. One lab is hoarding knowledge at the frontier. The other is giving it away. The gap in approach is now wider than the gap in capability, Open is only threatening when you are slow. Alibaba Qwen crossed 942 million downloads on Hugging Face by March 2026. Its share of new open weight derivatives went from 1% in January 2024 to 69% by February 2026. Chinese models now account for 30% of global model usage on aggregator platforms, up from 1% in late 2024. All under Apache 2.0 or MIT licenses, fully permissive. US frontier labs are spending $700 billion on capex while keeping the developmental knowledge locked. China is spending a fraction and giving the knowledge away. Adoption follows access, not origin. Now China too going to do 230 billions+ capex as per report i think... Fable 5 and Mythos 5 are the same model. Mythos goes to 200 approved partners. Fable goes to everyone else, with hidden capability limits on frontier ML work. The stated reason is safety. The result is that US labs build the best tools and then weaken them for the work that advances AI. DeepSeek V4 matches Opus 4.7 on agentic benchmarks and ships under MIT license with full weights. The question is not who builds the better model. It is who gets more people building with it. Some of the Stuff I took from SemiAnalysis, But this will go Nuclear way I don't know submitted by /u/ramanpalkuri9 [link] [comments]

Demo3：Browser Agent https://t.co/OsvOfyLS6D

Demo3：Browser Agent https://t.co/OsvOfyLS6D

Demo2: Multimodal Interactive Hybrid Agent https://t.co/GYGgYjeoyJ

Demo2: Multimodal Interactive Hybrid Agent https://t.co/GYGgYjeoyJ

Demo1：Multimodal Interactive Hybrid Agent https://t.co/e5hzk8bKo2

Demo1：Multimodal Interactive Hybrid Agent https://t.co/e5hzk8bKo2

Performance2：Multimodal Benchmarks Qwen3.7-Plus’s multimodal improvements are not limited to isolated gains in visual understanding. Instead, they reflect a systematic enhancement of the core capabil

Performance2：Multimodal Benchmarks Qwen3.7-Plus’s multimodal improvements are not limited to isolated gains in visual understanding. Instead, they reflect a systematic enhancement of the core capabilities required by multimodal agents—understanding complex visual inputs, https://t.co/QINqIV9lMU

Performance1：Text Benchmarks Qwen3.7-Plus delivers competitive text performance that approaches Max-tier models across the board. https://t.co/irPhTOWN98

Performance1：Text Benchmarks Qwen3.7-Plus delivers competitive text performance that approaches Max-tier models across the board. https://t.co/irPhTOWN98

👏👏 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 a

👏👏 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 https://t.co/T3YTDnkE1D

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]

📢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.🏃🏃

📢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.🏃🏃

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.

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

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RT @opencode: Qwen3.7 Max now available in Go - text only - 1M context - smartest model in the Qwen family to date

RT @opencode: Qwen3.7 Max now available in Go - text only - 1M context - smartest model in the Qwen family to date

🙌🙌

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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 the board! @arena More to ship. Stay tuned. 🕶️

🚀🚀 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. 🕶️

✅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. 🙌

✅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 🔗 ：https://t.co/3hSs6zquBH