Reflection releases Beam, a 501B open-weight model

Reflection has introduced Beam, a 501-billion-parameter language model released with downloadable weights. According to the company's announcement, the model is positioned as a frontier-scale system built around text generation and reasoning.

Beam uses a mixture-of-experts (MoE) architecture, a design that activates only a subset of its parameters for any given token. That approach lets a model carry a very large total parameter count — here, 501B — while keeping the compute cost of each inference step more manageable than a dense model of equivalent size.

Why it matters

Open-weight releases at this scale remain relatively rare, and most frontier-class reasoning models are kept behind APIs. Making the weights available changes who can study, fine-tune, and deploy the model.

• Open weights: the model can be downloaded and run outside a vendor's API.
• MoE efficiency: a large total parameter count with sparser active compute.
• Reasoning focus: positioned for text and reasoning workloads rather than a single narrow task.

The release is licensed under terms the company describes separately, so teams considering commercial use should review the specifics before building on it. For a group's first frontier-scale entry, Beam is a notable signal that open-weight development continues to push into larger, reasoning-oriented territory.