Reflection AI releases Beam, a 501B-parameter open-weight model
Reflection AI released Beam, its first open-weight model, with Apache 2.0-licensed weights on Hugging Face and BF16, FP8 and NVFP4 checkpoint variants. Beam is a sparse mixture-of-experts model with 501B total parameters, 23B active parameters per token, and a reported effective context length of 1 million tokens. It targets coding, agentic and long-horizon tasks; training used longer reinforcement-learning rollouts plus a length penalty to discourage unnecessary tokens, and users can tune a reasoning-effort parameter. Day-0 support is available through Baseten Model APIs and Dedicated Inference and via Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio), with multilingual and multimodal releases planned for later.
It pushes open weights to very large MoE scale and is pitched against Chinese open models, giving teams that want self-hosted, customizable coding and agentic models another base option.