Reflection AI Releases Beam, a 501B-Parameter Open-Weight MoE Model
Reflection AI has unveiled Beam, an open-weight sparse Mixture-of-Experts (MoE) AI model featuring 501 billion total parameters and 23 billion active parameters per token. Highlighted in Latent Space's AI News edition, Beam is specifically engineered for high-performance coding, reasoning, and agentic workloads. Beam represents a notable achievement for the American open-source AI ecosystem, providing developers with powerful open weights to build sophisticated agentic systems. By offering high capacity alongside efficient active parameters, it helps reduce reliance on proprietary closed-source model APIs. The model uses a sparse Mixture-of-Experts architecture (501B-A23B), enabling it to maintain manageable inference costs by routing each token to only 23 billion parameters out of its total 501 billion. It is a text-only model optimized for complex code generation, logical reasoning, and autonomous multi-step execution.
## BACKGROUND
A Mixture-of-Experts (MoE) architecture divides model capacity into specialized sub-networks ('experts') and routes each token to only a fraction of the total parameters, achieving high capabilities with faster generation speeds. Open-weight models grant developers full access to model parameters, enabling local deployment, customization, and fine-tuning without data privacy concerns.