Beam: Reflection's 501B open-weight model
Reflection has announced Beam, a new 501-billion parameter open-weight Mixture-of-Experts model designed for coding and agentic tasks. The model emphasizes inference efficiency, achieving performance comparable to larger models while using significantly less compute.
Why it matters
The release of high-performance, efficient open-weight models challenges the dominance of proprietary frontier models and lowers the barrier for developers to build complex AI agents.
We are introducing Beam, Reflection’s first open-weight model. Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.
Beam’s capabilities come from major investments in both pretraining and reinforcement learning (RL). We pretrained the model on 23.8 trillion diverse, curated, high-quality tokens from the web and proprietary licensed datasets, matching or outperforming available similar-sized open base models. In parallel, we developed the algorithms, training environments, and infrastructure needed to sustain high-compute RL at exceptional scale. Our high-compute RL run generated over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over 4 weeks of training.
Together, these efforts produced competitive open-weight performance with frontier inference compute efficiency.
Beam is undergoing final red-teaming and evaluations. You can sign up here for early access to the model. We will release the weights, technical report, model card, and developer artifacts later this month.
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