Time to give agents a hard task, introducing pmpp-hard! 69 GPU kernel tasks, 11 models, 3.1k agent rollouts and 5.8B+ tokens later we have the results. Kimi K3 claims the first place with a 0.71 score. Without dealing with if your task is “frontier” or not, it just solves them

It's a good time to share a new @interconnectsai project we made to help make sense of the accelerating open-weight releases these days. The Artifacts Hub builds on our monthly open model roundups and daily monitoring of every model on @huggingface. The new free resources are: 1. The Artifacts Hub — a curated view of the models trending on Hugging Face, highlighting inference tokens via Open Router, model intelligence via Artificial Analysis, and our tailored adoption metrics building on top of Hugging Face’s data. 2. Our Adoption Dashboard — a living dashboard of download and derivative model numbers by geography and organization. This highlights the US-China gap and growing players in the open ecosystem. To date, our primary efforts on Interconnects have been release recaps for popular models like Kimi K3, GLM 5.2, DeepSeek R1, etc. and monthly round-ups of the open models that matter, Artifacts Log. We’re expanding on these, building on the tools and internal data we’ve collected for other projects like The ATOM Project (and report). This allows us to capture our ecosystem view of open models, develop methods for understanding adoption of giant MoE models, and everything in between. We’re sharing them freely to help the open ecosystem find its strengths and grow. The Artifacts Hub right now covers 792 models released in the last two years, across the core text-focused language models and multimodal generative models. At Interconnects we follow the data of every model on Hugging Face, analyze the core few thousand LLMs (this list is public on GitHub and regularly updated), and hand select these core few hundred for further explanation. For the most popular models, the Hub let’s you quickly see how far behind the model was in terms of frontier intelligence based on Artificial Analysis’s Intelligence Index, compare Hugging Face and Open Router adoption to similar models, glance at relative adoption metric (RAM) scores for time-size normalized downloads, or look at the VAIL similarity index of models with related generations. A snapshot for what you’d see for something like GLM-5.2 is below. Thanks to @mnshah at VAIL for encouraging us to make this and @huggingface, @OpenRouter, & @ArtificialAnlys to making such useful data openly available.

Artifacts 23: Laguna S2.1, Inkling, & Kimi K3 show the utility of open models on the Pareto frontier. In this issue, a total of 24 models from june/july you should be aware of, from: Thinking Machines @thinkymachines (2) Tencent @TencentGlobal (2) Poolside @poolsideai (2) DeepSeek @deepseek_ai Moonshot AI @Kimi_Moonshot Meituan LongCat @Meituan_LongCat Motif Technologies Swiss AI Initiative @apertusllm AMD @AMD Upstage @upstageai InclusionAI @TheInclusionAI Moondream @moondreamai Baseten @baseten IBM @IBM Mistral AI @mistralai Google @GoogleAI Nanbeige @nanbeige Kwaipilot @KwaiAICoder InternLM @intern_lm Microsoft @Microsoft fal @fal Read the issue below.

Releasing weights indiscriminately isn't safe. Neither is keeping capable models inside a few labs. We think there's a path between them. We haven't mapped all of it. Our new post covers the part we can see: how we assessed Inkling, and why access should widen in stages.