Reflection’s Beam model hits 501 billion parameters

Reflection has introduced Beam, a 501 billion parameter sparse Mixture-of-Experts model trained to excel at coding, reasoning, and agentic tasks. The model’s development involved a substantial investment in computational resources; a four-week reinforcement learning run used 10.5K NVIDIA GB300 GPUs to generate over 100 million rollouts. Beam was pretrained on 23.8 trillion tokens, achieving performance competitive with GLM 5.2 and approaching Qwen 3.8-Max on key tasks while offering efficiency advantages over Kimi K3. These results, the company states, translate into “more intelligence per token,” delivering strong capabilities at a lower cost. Reflection detailed a novel approach to evaluating model performance, moving beyond simple parameter counts to assess generalization ability through an experiment. The team tasked Beam with generating a grid representing longitudes and latitudes, a puzzle appearing only days before the evaluation and therefore absent from the training data; Beam achieved 95.5% accuracy, positioning it between the Opus 5 and Fable 5 models in this specific test of novel task completion, Reflection says. This assessment method, focusing on out-of-sample generalization, provides a more detailed understanding of Beam’s capabilities than raw parameter size alone. The development of Beam incorporated a multi-tiered safety and alignment strategy, prioritizing adherence to defined principles during…