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TL;DR
Reflection has released Beam, a text-only, 501-billion-parameter mixture-of-experts model under the Apache 2.0 license. The company lists coding and agentic work as targets and claims a context window of up to 1 million tokens, but the release materials provide no independent performance results and describe a BF16 checkpoint of about 1 terabyte.
Reflection has released Beam, a text-only, 501-billion-parameter mixture-of-experts model, on Hugging Face under the Apache 2.0 license. The company says Beam is intended for coding and agentic tasks and supports a context window of up to 1 million tokens, making it available for developers to test, adapt and run with supported tools. The announcement does not include independent benchmarks, and its stated scale comes with substantial hosting requirements.
Beam uses a mixture-of-experts (MoE) architecture, which routes each token through a subset of the model’s experts. Reflection’s release materials list 23 billion active parameters per token, out of 501 billion total. The model has 52 layers; each MoE layer contains 256 experts, with eight selected for each token, according to the announcement. This limits the parameters involved in an individual token’s computation, but does not eliminate the need to keep the model’s full expert weights available.
The deployment figures underline that distinction. The release puts the BF16 checkpoint at about 1 terabyte in safetensors format and lists BF16, FP8 and NVFP4 versions. It identifies NVFP4 as intended for NVIDIA Blackwell GPUs and FP8 versions for Hopper and Ampere chips. The materials do not provide a complete minimum-hardware or hosting-cost estimate, or quantify quality and speed trade-offs among formats.
Beam accepts and produces text and lists tool calling, configurable reasoning settings and a feature called “preserve_thinking” for carrying reasoning across turns. It has day-one support in Transformers, vLLM and SGLang, as well as Reflection’s Rust-based harness. The release also describes recipes for inference, supervised fine-tuning and reinforcement learning, including full-parameter reinforcement learning with Miles.
What Beam Opens to Developers
The Apache 2.0 release gives developers permission to use, modify and redistribute Beam subject to the license terms. That can let teams examine the model, adapt it for internal workflows, and choose whether to run it on their own infrastructure or through a cloud provider. Integrations and training recipes give technical teams starting points for experimentation, though they do not remove the need for substantial compute and engineering work.
Beam’s scale and architecture make its practical costs a key part of evaluating it. 23 billion active parameters describes the subset used for each token, not the full memory footprint; the announced BF16 checkpoint is about 1 TB. Smaller numerical formats may suit different hardware, but the source does not report their measured impact on output quality, latency or resource use.
Reflection and Hugging Face also announced $10,000 in Hub credits for selected projects using Beam. The listed eligible work includes demos, quantizations, datasets, reinforcement-learning environments and benchmarks shared on Hugging Face. Those projects could provide examples of how developers use the model, but demonstrations or company-provided recipes are not a substitute for comparable, independent evaluations.
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Architecture Behind the Million-Token Claim
Reflection describes Beam as its first model and presents it as designed for coding and agentic work, where a system may use tools or handle multi-step tasks. The model’s announced 1-million-token context window is a capability stated in the release, rather than an independently established performance result. The materials provided do not show how accuracy, speed or cost change as input length grows.
The release describes a hybrid attention design: 43 layers use a 512-token sliding window, while nine layers can attend across the full context. It also says the eight selected experts account for 98.4% of the model’s parameters. These architectural details explain how Reflection says Beam handles long inputs and sparse computation; they do not by themselves establish how well it performs on particular tasks.
To support testing beyond inference, the release includes recipes for supervised fine-tuning and reinforcement learning. Reflection and Hugging Face say they worked on support for running and adapting the model. The Apache 2.0 license and published integrations give outside developers routes to explore the model, while its size may limit testing to teams with access to suitable infrastructure.
“501B-parameter MoEs LLM with 23B active per token.”
— Reflection and Hugging Face, in Beam’s release materials
Performance and Hosting Questions
The supplied announcement provides no independent benchmark results or direct comparisons with other models. It does not establish how Beam performs on coding or agentic tasks, nor does it report quality, latency or memory measurements for use at the claimed 1-million-token context length. Those questions require evaluations that disclose tasks, prompts, hardware and measurement methods.
Practical deployment needs also remain partly unspecified. The release gives a roughly 1 TB BF16 checkpoint size and names hardware generations for FP8 and NVFP4 formats, but does not set out a full minimum configuration or cost estimate. It also does not quantify how lower-precision versions affect performance or output quality. The context-window figure and feature descriptions should therefore be read as claims in the release materials, not as independent validation.
The materials do not specify when credit-award decisions will be made. They say selected projects will receive Hub credits and that qualifying project links will be collected through Beam’s Hub page, but provide no judging timetable in the supplied account.
Independent Tests and Community Projects
Developers can use the Hugging Face release, supported inference libraries and published recipes to test Beam, adapt it or build projects around it. The next useful evidence will come from reproducible evaluations that report hardware, context length, task selection, latency and resource use. Such results would help users judge the model’s coding and agentic capabilities and the practical cost of serving it.
Reflection and Hugging Face say they will collect links to qualifying projects shared on the model’s Hub page, select projects and distribute the announced $10,000 in credits to winners’ Hub accounts. The supplied materials give no date for the selection or awards. Until additional measurements are available, Beam’s announced architecture, integrations and context limit describe what developers can investigate, not a verified ranking or performance outcome.
Key Questions
What is Beam?
Beam is a text-only, 501-billion-parameter mixture-of-experts model released by Reflection on Hugging Face under the Apache 2.0 license. Reflection says it is aimed at coding and agentic tasks.
Does Beam use all 501 billion parameters for every token?
No. The release says 23 billion parameters are active per token, with eight experts selected from 256 in each MoE layer. The full model’s experts still need to remain available in memory, according to the release materials.
What hardware does Beam require?
The release lists a BF16 checkpoint of about 1 terabyte and also offers FP8 and NVFP4 formats, naming target GPU generations for those versions. It does not provide a complete minimum-hardware or cost breakdown, so requirements depend on the format, serving setup and workload.
Has Beam’s performance been independently tested?
The supplied announcement does not include independent benchmarks or comparisons. It states a context window of up to 1 million tokens but gives no independent results for accuracy, speed or memory use at that length.
How can developers try or adapt Beam?
The release lists support for Transformers, vLLM and SGLang, alongside Reflection’s Rust-based harness, and describes inference, fine-tuning and reinforcement-learning recipes. Reflection and Hugging Face also announced $10,000 in Hub credits for selected projects using Beam; the supplied materials do not give an award timetable.
Primary source: Hugging Face · via ThorstenMeyerAI.com
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