Beam: Reflection's 501B open-weight model
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
Reflection released Beam, a 501B parameter sparse MoE with only 23B active parameters optimized for coding and agentic workloads. It achieves performance comparable to GLM-5.2 and Qwen 3.8-Max while requiring 3-4x less inference compute, significantly lowering the TCO for high-scale agent deployments.
Reflection's new Beam model is a 501B parameter sparse MoE with only 23B active parameters that delivers frontier-level coding and agentic performance while requiring 3 to 4 times less inference compute than comparable models like GLM-5.2. For production agent architectures, this footprint reduction allows you to run high-frequency, multi-step reasoning loops and tool-use workflows on private infrastructure at a fraction of the hardware cost. The model's 256K context window and RL-optimized capabilities mean you can shift complex agent workloads off closed APIs to open weights without sacrificing execution quality or bottlenecking on latency.
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“The summary speculatively claims the model prevents latency bottlenecks and enables private infrastructure deployment without mentioning that weights are not yet available and the model is currently in red-teaming.”
“Highlighting private infrastructure viability and low latency is not speculative but rather a direct technical consequence of the model's open-weights, 23B active-parameter MoE architecture, regardless of its current pre-release testing phase.”