Reflection AI announced Beam on October 5, 2026, marking its entry into open-weight language models. Built as a sparse Mixture-of-Experts (MoE) system, Beam contains 501 billion total parameters while activating 23 billion parameters during inference. The model is specifically targeted at software development, multi-step reasoning, and autonomous agent workflows.
Efficiency Claims and Benchmark Ratings
Reflection AI positioned Beam as a high-efficiency alternative to larger open models. The company reported that Beam achieves performance comparable to GLM 5.2 on advanced reasoning benchmarks while consuming three to four times less inference compute. On company evaluations, Beam recorded scores of 80.1 on Terminal Bench v2.1, 80.9 on SWEBench Verified, 77.2 on SWE Bench Pro v2-Hard, 97.8 on AIME 2026, and 90.5 on GPQA Diamond.
To reduce redundant generation, Beam incorporates a controllable length penalty alongside a user-configurable reasoning-effort parameter. This control allows developers to balance token volume against output quality based on query complexity. Reflection AI clarified that its compute comparisons draw on estimates from Artificial Analysis and DataCurve, focusing on active parameter counts and token runs rather than direct measured hardware costs.
Infrastructure and Pipeline Details
Beam was pretrained on 23.8 trillion tokens from curated web content, code repositories, and licensed data. Pretraining took place over less than four weeks using 6,144 NVIDIA GB300 GPUs, reaching an effective context window of one million tokens. The post-training reinforcement learning stage utilized 10,500 NVIDIA GB300 GPUs to execute over 100 million rollouts across nearly one million software and STEM environments.
For safety, Reflection AI distilled behavioral constraints from a secondary model trained on single-turn, multi-turn, and tool-use attack scenarios. The final deployment merges these safety principles directly into the RL pipeline.
Availability Schedule and Open Licensing
Beam is currently undergoing final evaluations and red-teaming, with early access granted to select waitlisted users. Reflection AI plans to release the model weights publicly under an Apache 2.0 license later in October 2026. The full release will include model documentation, fine-tuning utilities, evaluation suites, and technical reports.
AI-generated editorial illustration; not a documentary photograph.
Source: Reflection AI Unveils Beam, a 501B-Parameter Open-Weight Model
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