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撮影の時は清楚に…✨ 新人グラドル #遠山茜子# のデビュー㊙話に迫る❗ 週プレ編集部(@shupure)で #MAX「GET# MY LOVE!」をダンス❤ 『紺野、今から踊るってよ』 🎶無料配信中👉 @odorutteyo

ギャルオーディション・グランプリ美女と踊る🎉 #MAX「GET# MY LOVE!」😆💘 『紺野、今から踊るってよ』 🎶無料配信⇒ @odorutteyo @akane_016

Cloud sessions are officially available and out of research preview! They let you keep Claude Code working, even when your laptop is closed. Existing subscribers get a one-time credit to try them: $100 on Pro, $250 on Max.

Grok 4.6 just tied for #1# on the Artificial Analysis Agentic Index • Grok 4.6 (high) — 59 • Claude Opus 5 (max) — 59 Outperforming Claude Fable 5 and GPT-5.6 Sol We’re entering the agentic era, and this is exactly the kind of benchmark that matters so much: tool use, planning, autonomy and complex problem solving Grok 4.6 is now sitting at the very top And that matters even more as Grok powers Grok Build and Grok Bot, where the model has to go beyond answering questions and actually take actions, use tools and complete real work Grok’s agentic capabilities are getting seriously powerful

Agentic work is where @grok 4.6 lands hardest, taking the top spot on the Artificial Analysis Agentic Index at 59, tied with Claude Opus 5 Max. The index measures tool use, planning, autonomy and complex problem solving rather than single answers Grok 4.6 completes tasks in ~53 turns and ~0.5bn input tokens on average, against ~103 turns and ~2.0bn for Claude Opus 5 Max Cost of $0.84 per task, putting it on the intelligence versus cost per task Pareto frontier Enterprises buying agents pay per completed task, not per benchmark point. Turn efficiency is what determines whether a long-running workflow is affordable at volume. Two labs now sit at the top of this index with very different cost structures. Buyers get real choice on price for the first time in agentic deployment.

America’s newest and most exciting aerospace technology expo and air show is landing @NASAKennedy in Florida on Nov. 7-8. Make plans to join us for MAX POWER! Get the details:

Introducing Qwen 3.8-Max, the most capable model in the Qwen family to date—scales to 2.4 trillion parameters, delivering comprehensive improvements across coding, work, research, and long-horizon tasks. Get reliable results for challenging questions and complex tasks. Try today!

Introducing Qwen 3.8-Max, the most capable model in the Qwen family to date—scales to 2.4 trillion parameters, delivering comprehensive improvements across coding, work, research, and long-horizon tasks. Get reliable results for challenging questions and complex tasks. Try today!

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

Why manage separate AI tools for the script, the image, and the video? The Alibaba Cloud Token Plan gives you one shared credit pool across supported models and tools, with visibility into usage and access to newer models like Qwen3.8-Max-Preview, HappyHorse1.1, DeepSeek V4, and GLM-5.2. One plan for every modality, to build more, spend less. Get started from just $4 in your first month. Explore the Token Plans: #AlibabaCloud# #TokenPlan# #Qwen# #Wan# #HappyHorse# #GenerativeAI# #AIContent# #MultimodalAI#