Beam by Reflection AI

Beam is Reflection AI's first announced model with a planned open-weight release, announced as a sparse Mixture-of-Experts model with 501 billion total and 23 billion active parameters, built for coding, reasoning, and agentic workloads. As of the announcement, Beam is in final red-teaming and evaluation, with early-access signup available through the Reflection platform.

What It Is

Beam is a text-only large language model from Reflection AI. It was trained with a focus on coding and agentic tasks such as terminal use, software engineering, tool calling, and web search. Although it is not multimodal, it can work with information from other modalities when that information is represented as text. Reflection says it was pretrained on 23.8 trillion tokens from the web and proprietary licensed datasets, and that midtraining extends its effective context length to 1M tokens.

Training and Efficiency Claims

Reflection describes a high-compute reinforcement learning run that generated over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks, using nearly one million environments across software engineering, terminal use, STEM, web search and tool use. Per Reflection, Beam is competitive with larger open models such as GLM 5.2 and approaches Qwen 3.8-Max on coding and agentic tasks, while frontier open models such as Kimi K3 remain ahead on raw capability. Reflection also states that Beam reaches reasoning scores comparable to GLM-5.2 using 3–4x less inference compute. These are vendor-published benchmark and efficiency results, not independent validation.

Reasoning Effort and Behavior

Beam includes a reasoning effort parameter: lower settings favor shorter responses, while higher settings allow longer reasoning for demanding tasks. Reflection's demos include a live NYC subway map, a 3D browser game, and a fine-tuning notebook created with Beam plugged into OpenCode.

Current Status

Beam is available only through early-access signup. Reflection says it will release the weights, technical report, model card, and developer artifacts later in the announced month, and has stated an intention to release the weights under Apache 2.0 along with documentation and tooling for running, evaluating, and fine-tuning. These releases had not occurred at the time of the announcement. The Reflection API, documented separately, is in beta with gradual access via a waitlist and offers an OpenAI-compatible endpoint.

Access and Pricing

Public prices and free-access terms are not published. Apply for selective early access through https://platform.reflection.ai/ or contact Reflection at https://reflection.ai/contact to confirm availability and commercial terms. The contact-for-pricing listing does not establish a paid-access entitlement. Planned Apache 2.0 weights are not available to download yet.

Hosted API Limits

Current developer documentation lists Beam-501B-A23B with a 256K API context window and 128K maximum output; beta limits may change. The announcement’s 1M effective-context claim is distinct from the hosted API limit. API quotas apply at organization level and daily limits reset at 00:00 UTC. See https://developers.reflection.ai/models and https://developers.reflection.ai/rate-limits.