Qwen-3.8 vs Kimi-K3
Prompt: "SVG animation of a real tennis rally, two players going at it, physically accurate, follows real-world logic."
This one took me 2 hours to generate. Maybe that's on Qoder, not sure. Qwen-3.8 spent 40+ min just thinking and planning, then another hour writing the files and squashing bugs.
But the result? Genuinely clean. It's got sound, and a bottom bar showing live commentary, ball speed, rally count, and shot analysis.
Kimi-K3? No sound, and the stats bar is buggy. Doesn't update in real time.
Qwen-3.8 vs Kimi-K3
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Just found a legit 0-cost local solution for Agent search &
crawling I’ve been playing around with this open-source project called wigolo lately. It lets you do full search, scraping, crawling, extraction, and research—all running completely locally. No API keys, no cloud services, nothing leaves your device. This means your agents can search the web and grab pages for free, without relying on Firecrawl, Exa, Tavily, Brave, Jina, or any of those other paid tools. Super clean for regular users too—no more messing around with API keys or environment variables. github: KnockOutEZ/wigolo
Alibaba dropped Qwen3.8. Only Fable 5 is ahead of it right now.
2.4T params. Open source. Biggest Qwen model they've shipped. Qwen3.8-Max-Preview is already live. You can use it on Token Plan, Qoder, and QoderWork. No benchmarks out there yet. I'll test it myself later and share what I find.
Meituan shipped LongCat-2.5
. 1.6T params, ~48B active, native multimodal, aimed at long-horizon agents, trained start to finish on Chinese hardware. Images get quantized into multi-level discrete tokens, and that's where Visual Coding and 3D pick up. On the RL side, failed trajectories get turned back into verifiable training tasks. W4A4 inference, 43% faster prefill. Meituan's own evals put it between Claude Opus 4.8 and Opus 5.
SenseTime open-sourced SenseNova Loop-9B-Preview.
It stacks layers instead of parameters. The decoder has 32 layers, but layers 4 through 27 run twice with shared weights, so a forward pass does 56 layers of work while the checkpoint stays at 9B. Their own eval numbers: 71.38 on BIRD-Dev and 13.72 on AutomationBench, both above Qwen3.5-27B. Terminal Bench is still behind.
