Kini o ṣẹlẹ
Reflection AI announced Beam, its first open- model, featuring 501 billion total parameters and 23 billion active parameters. The model is currently in final red-teaming, with an early version available via waitlist. The company plans to release the weights under an Apache 2.0 license later in October 2026, alongside a technical report and full tooling stack.
Reflection AI introduced Beam on October 5, 2026, describing it as a sparse Mixture-of-Experts system with 501 billion total parameters and 23 billion active parameters. The model is specifically built for coding, reasoning, and agentic workloads. According to the source, Beam is undergoing final red-teaming and evaluations, with an early version currently offered to a select group of users through a waitlist. Reflection stated that Beam is the first in a series of models and that training for subsequent versions is already underway.
The company reported specific evaluation scores, including 80.1 on Terminal Bench v2.1, 80.9 on SWEBench Verified, and 97.8 on AIME 2026. Reflection claimed that Beam achieves scores comparable to GLM-5.2 on advanced reasoning benchmarks while using three to four times less inference compute. These efficiency estimates are based on Artificial Analysis and DataCurve data but are described by Reflection as approximate compute comparisons rather than measured inference costs, as they exclude prompt prefill and serving overhead.
Training details indicate Beam was pretrained on 23.8 trillion tokens using 6,144 NVIDIA GB300 NVL72 GPUs, completing the run in under four weeks. The campaign involved over 100 million rollouts on 10,500 GPUs, utilizing approximately 1.3 billion sandboxes. Reflection reported a goodput of 92.3 percent and noted that the model learned to query other LLMs and use OCR APIs during RL training, despite browsing tasks not being explicitly included in the training mixture.
For alignment, Reflection trained a second model from the same checkpoint using a separate pipeline focused on behavioral principles, which was then merged with the main model via multi-teacher on-policy distillation. The company stated it will publish safety evaluation results in the technical report and open-source its internal safety evaluations. Reflection also outlined commitments to releasing model weights, publishing research, and open-sourcing software, including RL tools and environments.
Kini idi ti o ṣe pataki
Beam represents a significant entry into the open- frontier, claiming competitive performance with larger models like GLM 5.2 and Qwen 3.8-Max while emphasizing inference efficiency. Its Apache 2.0 license and planned release of safety evaluations and RL tools could lower barriers for developers building agentic systems, though independent verification of its reported benchmarks and efficiency claims is pending.
The release of a 501B parameter open- model under the Apache 2.0 license is a concrete industry move that expands the availability of high-capability models for local and enterprise deployment. By positioning Beam as competitive with larger models like Qwen 3.8-Max on coding tasks while emphasizing inference efficiency, Reflection targets a practical pain point for developers seeking to reduce compute costs without sacrificing performance.
The planned release of the full stack for running, evaluating, and fine-tuning the model, along with open-sourced safety evaluations, addresses common gaps in open- releases. This approach may facilitate more rigorous independent verification of the model's capabilities and safety profile, which is currently limited to the company's self-reported metrics.
Reflection's background, including its certification as a consortium member of the Department of Energy’s Genesis Mission and its partnership with Shinsegae for a sovereign AI cloud in South Korea, suggests a strategic alignment with national AI infrastructure goals. This context adds to the model's potential impact on both commercial and public-sector AI deployments.
Ibaraẹnisọrọ Mechanism: Bii O Ṣe Nṣiṣẹ Lootọ
Ṣawari imọ-ẹrọ abẹlẹ lẹhin idagbasoke yii ni ibaraenisọrọ.
Which component of an AI application is the machine-learning model itself?
Kini lati wo tókàn
Monitor the official release of Beam's weights and technical report in late October 2026 to verify the reported benchmarks, particularly the 80.1 score on Terminal Bench v2.1 and the claimed 3-4x inference compute reduction compared to larger models.
The primary next step is the official release of Beam's weights and technical report later in October 2026. Independent researchers and developers will likely replicate the reported benchmarks, particularly the 80.1 score on Terminal Bench v2.1 and the 97.8 score on AIME 2026, to verify the company's claims.
Attention will also focus on the practical implementation of the 'reasoning-effort' parameter, which allows users to trade token usage against performance. Real-world testing will determine if the claimed 3-4x inference compute reduction holds up in production environments, including the overhead of prompt prefill and attention operations that were excluded from the initial estimates.
The open-sourcing of Reflection's internal safety evaluations and RL tools will be a key indicator of the model's safety posture. The community will scrutinize the adversarial safety dataset and the multi-teacher distillation process to assess the robustness of the model's alignment against jailbreaks and agentic misuse.