Reflection AI opens early access to Beam, a 501B open-weights model

Reflection AI is entering the open-weights market for the first time with Beam, a model it says was trained on 23.8 trillion tokens from web and proprietary licensed datasets. Rather than chasing the largest possible model, the company built Beam around coding, reasoning, and agent workloads, and says it matched or exceeded existing open base models of the same scale.

The training run itself was notable for its scale: Reflection AI says it generated more than 100 million rollouts across 10,500 NVIDIA GB300 GPUs over a four-week training period, and that it developed the algorithms, training environments, and infrastructure needed to sustain that high-compute reinforcement learning. That infrastructure claim is the foundation for the efficiency pitch that follows.

The efficiency pitch is where the competitive positioning lives. Reflection AI says Beam competes with large open models such as GLM-5.2 and approaches Qwen 3.8-Max on coding and agent tasks, while reaching comparable scores on advanced reasoning benchmarks at one-third to one-quarter the inference compute, with the gap widening against 2T+ parameter families. For developers weighing open models, the open question is whether independent testing confirms that efficiency advantage once the weights and technical report arrive this month, since the performance comparisons so far come from Reflection AI's own account.