Reflection unveils 501B-parameter Beam, trailing the latest top Chinese open-source models
Nvidia-backed AI startup Reflection has introduced Beam, its first open-weight model. Built on a mixture-of-experts, or MoE, architecture, Beam has 501 billion total parameters and activates 23 billion parameters per inference step. The model is aimed at coding, reasoning and agent-based workloads. Based on metrics released by Reflection, Beam performs close to GLM-5.2 overall, but it does not yet match the newest leading open-source models from China. On DeepSWE, Beam scored 44.4, compared with 44.0 for GLM-5.2 and 51.0 for Qwen 3.8-Max. GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash posted 61.0, 68.0 and 74.2, respectively. Reflection also disclosed the scale of Beam’s training. Pretraining used 6,144 Nvidia GB300 GPUs and processed 23.8 trillion tokens in under four weeks. That was followed by four continuous weeks of reinforcement learning on 10,500 GB300 GPUs, producing more than 100 million rollouts. The company said this ranks among the largest reinforcement learning training runs in the public record, to its knowledge. Beam remains in final safety testing, with only a small number of users getting early access. Reflection said it plans to release the full weights, a technical report and a model card later this month under the Apache 2.0 license.
