A 501-Billion-Parameter Model Where 23 Billion Do the Work

According to the write-up on AWS's machine learning blog, Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks such as maintaining code, using tools, interpreting results, and completing multi-step workflows. The numbers come from the launch material, so read them as the model's own pitch. The part worth learning is the parameter pair. Beam has 501 billion total parameters, and that total shapes checkpoint storage, weight memory, sharding, and parallelism — the question of whether the model fits and how it spreads across hardware. It has 23 billion active parameters, which influence per-token compute. One number describes the size of the thing you have to hold; the other describes the work done for each token you generate. A model can be enormous on disk and still comparatively light per token, which is why “how many parameters” is a less useful question than “how many are active.”