Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Nvidia's new Nemotron 3.5 Super VL is another step forward for US open-weight AI Nvidia's new models join a wave of open-weight releases from US labs on the heels of the Hugging Face hack. Radhika Rajkumar is a senior editor at ZDNET based in New York City. Three new Nvidia open models strive for efficiency and speed. Open models are having a renaissance on the heels of the Hugging Face hack.

Open models have real benefits. Do they outweigh the risks? Late last month, Anthropic released a report arguing that the model GLM-5.3 The report's core concern is that the powerful new model, released in August by the Chinese company Z.ai, is freely available for anyone to download and modify. Anthropic's assertion is only the latest move in a long-running debate about whether frontier AI models should be made open, like GLM-5.3, or should stay locked behind the doors of the frontier labs. These days, much of the debate about how to handle open models is playing out in public--and there's a lot of money resting on its outcome.

Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Mistral's new Le Chonk model brings AI cybersecurity to your business - and you control it Open models are vying to become the everyman defense solution for a growing swarm of agent breaches. Radhika Rajkumar is a senior editor at ZDNET based in New York City. Mistral's open-weight ML4 model Le Chonk is in preview. Open models are positioned as democratic defenders from AI attacks.

Open models may soon be added to an updated AI framework, sources tell WIRED, as the White House continues to grapple with how to regulate a technology it has tried not to regulate. White House officials are almost certain to revise the Trump administration's new artificial intelligence guidelines and expand its oversight of AI models, according to people familiar with the matter. The White House announced this month that it had developed an AI framework, where the most powerful models created by US labs would be tested for safety by the federal government before they could be released publicly. The administration has not made the framework public, and reportedly has no plans to do so. The AI framework currently deals only with so-called closed models developed by the likes of Anthropic and OpenAI .

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI Our AI Model Release Tracker keeps new models in context with their peers, so you know which are worth your time. AI labs are shipping new models nonstop. Besides being better and faster than their predecessors, not every new model is guaranteed to be a major step change, despite how the company's PR may wax poetic about them. Model strengths really emerge in context: Where are competitor models lacking or excelling? Which models have outstanding specialties, and which are just catching up to industry standards? Our Model Release Tracker helps you make sense of where models stand relative to each other and whether they're worth a deeper look. While we don't test every model or model update on this list, we'll always include the key elements you need to know, along with our hands-on expert test, where applicable.

NVIDIA has gathered together some of the biggest tech companies in the world to form the Open Secure AI Alliance with the goal of improving cybersecurity. Among the 27 founding members are Microsoft, SpaceX, Dell, The Linux Foundation and NVIDIA itself. "The Open Secure AI Alliance -- building on the leadership of the Linux Foundation's Akrites initiative and OpenSSF community work -- will work to remediate and disclose vulnerabilities using open technologies," NVIDIA wrote. The group argues that to best defend against attacks in the age of AI models like Anthropic's Mythos 5, security researchers need access to both open and closed frontier models. As a prime example, NVIDIA cited OpenAI's recent rogue attack on Hugging Face.

In the AI boom, chatbots and GPTs come and go quickly. On a drizzly and windswept afternoon this summer, I visited the headquarters of Rokid, a startup developing smart glasses in Hangzhou, China. As I chatted with engineers, their words were swiftly translated from Mandarin to English, and then transcribed onto a tiny translucent screen just above my right eye using one of the company's new prototype devices. Rokid's high-tech spectacles use Qwen, an open-weight large language model developed by the Chinese ecommerce giant Alibaba. OpenAI's GPT-5, Google's Gemini 3, and Anthropic's Claude often score higher on benchmarks designed to gauge different dimensions of machine cleverness.

The world's top chipmaker wants open source AI to succeed--perhaps because closed models increasingly run on its rivals' silicon. Nvidia CEO Jensen Huang arrives for a meeting with lawmakers in Washington, DC. Nvidia has made a fortune supplying chips to companies working on artificial intelligence, but today the chipmaker took a step toward becoming a more serious model maker itself by releasing a series of cutting-edge open models, along with data and tools to help engineers use them. The move, which comes at a moment when AI companies like OpenAI, Google, and Anthropic are developing increasingly capable chips of their own, could be a hedge against these firms veering away from Nvidia's technology over time. Open models are already a crucial part of the AI ecosystem with many researchers and startups using them to experiment, prototype, and build.

Depending on foreign-made open models is both a supply chain risk and an innovation problem, experts say. Since 2022, America has had a solid lead in artificial intelligence thanks to advanced models from high-flying companies like OpenAI, Google DeepMind, Anthropic, and xAI. A growing number of experts, however, worry that the US is starting to fall behind when it comes to minting open-weight AI models that can be downloaded, adapted, and run locally. Open models from Chinese companies like Kimi, Z.ai, Alibaba, and DeepSeek are now rapidly gaining popularity among researchers and engineers worldwide, leaving the US as a laggard in an increasingly vital area of AI innovation. "The US needs open models to cement its lead at every level of the AI stack," Nathan Lambert, founder of the ATOM (American Truly Open Models) Project, tells WIRED.

Large language models (LLMs) have achieved impressive results in high-resource languages like English, yet their effectiveness in low-resource and morphologically rich languages remains underexplored. In this paper, we present a comprehensive evaluation of seven cutting-edge LLMs -- including GPT-4o, GPT-4, Claude~3.5~Sonnet, LLaMA~3.1, Mistral~Large~2, LLaMA-2~Chat~13B, and Mistral~7B~Instruct -- on a new cross-lingual benchmark covering \textbf{Cantonese, Japanese, and Turkish}. Our benchmark spans four diverse tasks: open-domain question answering, document summarization, English-to-X translation, and culturally grounded dialogue. We combine \textbf{human evaluations} (rating fluency, factual accuracy, and cultural appropriateness) with automated metrics (e.g., BLEU, ROUGE) to assess model performance. Our results reveal that while the largest proprietary models (GPT-4o, GPT-4, Claude~3.5) generally lead across languages and tasks, significant gaps persist in culturally nuanced understanding and morphological generalization. Notably, GPT-4o demonstrates robust multilingual performance even on cross-lingual tasks, and Claude~3.5~Sonnet achieves competitive accuracy on knowledge and reasoning benchmarks. However, all models struggle to some extent with the unique linguistic challenges of each language, such as Turkish agglutinative morphology and Cantonese colloquialisms. Smaller open-source models (LLaMA-2~13B, Mistral~7B) lag substantially in fluency and accuracy, highlighting the resource disparity. We provide detailed quantitative results, qualitative error analysis, and discuss implications for developing more culturally aware and linguistically generalizable LLMs. Our benchmark and evaluation data are released to foster reproducibility and further research.