❯ Reflection AI releases weights for 501-billion-parameter Beam, built to finish tasks with fewer tokens

First model, open weightsAccording to VentureBeat, Reflection AI released the weights of its first model, Beam, under Apache 2.0 on October 16, with 501 billion total parameters and 23 billion active per token. Artificial Analysis said on X that Beam scores 30 on its Intelligence Index, second among US open-weight models.

Penalizing extra tokensReflection added a penalty for unnecessary tokens during reinforcement learning while keeping the model from skipping tasks to save words. Training ran four weeks, over 100 million rollouts, on 10,500 Nvidia GB300s. The company says Beam scores about 80.1% on Terminal-Bench 2.1 using about 25,000 tokens per attempt, while GLM-5.2 scores slightly higher with about 80,000; DeepSeek V4.1 Flash scores 90.6%, ahead of Beam.

Another option for enterprisesReflection is building open frontier models and an operating system for agents, with Nvidia as an investor. The 8-bit and 4-bit versions fit on one eight-GPU server. Companies deploying their own models can do the math: shorter outputs mean the same hardware serves more requests.

▮ SIGNALOpen models are competing on a new dimension: not just who is smarter, but how many tokens it takes to get the same job done.