Reflection AI, the Brooklyn startup founded by former DeepMind researchers and backed by Nvidia, unveiled Beam on Monday, its first frontier open-weight model. Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active per token, trained on 23.8 trillion tokens in under four weeks on 6,144 Nvidia GB300 GPUs.

The company's pitch is efficiency, not raw capability: Reflection says Beam matches Z.ai's GLM-5.2 on advanced reasoning benchmarks while using three to four times less inference compute, positioning it as a Western-built answer to cheaper Chinese open models. Its own reported numbers still show newer Chinese models like GLM-5.3 and Kimi K3 ahead on most tests, and no independent results exist yet.

Beam is available today as a waitlisted early-access API, with the Apache 2.0 open weights promised later in October. Text-only with a 1 million-token context window, it is aimed squarely at coding and agentic workloads, where Reflection claims it outperforms Western open rivals like Thinking Machines Lab's Inkling.