Nvidia-backed Reflection AI announces Beam, a new ultra-efficient open model that boasts capabilities on par with those of Chinese ones

Beam outperforms competing Western open-source systems like Thinking Machines' Inkling across key software engineering benchmarks.

A new open-weight model just entered the artificial-intelligence landscape, and this time it's from an American lab.

On Monday, Reflection AI announced its first open-weight model, called Beam, which is specialized for coding and agentic performance. Beam boasts 501 billion parameters, or adjustable numerical values.

The model is going through final evaluations, with early access available to select parties.

"Beam advances the Western open-weight frontier and is competitive with larger open models like GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks," the company wrote in a blog post, referring to models made by Chinese companies Zhipu (HK:2513) and Alibaba (BABA).

The launch of Beam is a step forward for American AI capabilities, as the open-weight landscape has been dominated by Chinese players. Founded by ex-Google (GOOGL) (GOOG) DeepMind researchers in 2024, Reflection AI is dedicated to building open systems. The startup counts Nvidia (NVDA) as a major backer.

"We're already training the next, larger model," Reflection co-founder and President Ioannis Antonoglou said in a Monday X post.

Open models have emerged as a cheaper alternative to closed models. Their weights, or the numeral parameters that the model learns throughout the training process, can be downloaded directly and run locally. Beam's weights will be available later this month.

Beam is said to surpass other American open-weight models such as Thinking Machine's Inkling and Nvidia's Nemotron 3 Ultra across different agentic coding and reasoning benchmarks.

Although Kimi K3, the open-weight model developed by Chinese startup Moonshot AI, remains "ahead on raw capability," Beam holds an advantage with efficiency at inference time, according to Reflection. That means it uses less computing, or processing power, to perform real-time workflows. Although the model features 501 billion parameters, it only activates 23 billion parameters per query, giving users the advantage of a large, powerful model without the high computing costs.

On advanced reasoning benchmarks, Beam can achieve comparable scores to GLM-5.2 with three to four times less processing computing, according to Reflection.

Also: Here's what Nvidia's $13 billion Hugging Face deal means for the world of AI

-Christine Ji