The much anticipated open-source model from Reflection AI just dropped.

Beam now becomes the strongest western open model, rivals GLM-5.2 while using 3-4x less inference compute.

The 3-4x efficiency edge over GLM-5.2 rests on estimated forward-pass FLOPs (2 × active parameters × generated tokens), which leave out prefill, attention and serving overhead, so it is not a measured cost.

A sparse mixture-of-experts model with 501B total and 23B active parameters, pretrained on 23.8T tokens in under 4 weeks on 6,144 GB300 GPUs, with a 1M-token effective context.

The RL run used 10.5K GB300 GPUs for 4 weeks, produced over 100M rollouts across nearly 1M environments and about 1.3B sandboxes, and ended with no sign of a plateau.

Beam nearly ties GLM-5.2 at 80.1 versus 81.0 on Terminal Bench v2.1, but trails DeepSeek V4.1 Flash at 90.6 and Kimi K3 at 88.3, neither of which appears in the headline chart.

and Reflection will also ship FP8 and NVFP4 builds under Apache 2.0.