Reflection AI announced Beam on October 5, 2026, as its first open-weight model. The company describes it as a sparse Mixture-of-Experts (MoE) model with 501 billion total parameters and 23 billion active parameters, designed for coding, reasoning, and agentic workloads. At announcement time, the weights were not yet available: Reflection said they were undergoing final red-teaming and evaluation, with release planned for later in October.
What is Reflection AI’s Beam model?
Beam is a large language model built using a sparse Mixture-of-Experts architecture. In an MoE model, only a portion of the model’s parameters is active for a given input. Reflection reports 501 billion parameters in total and 23 billion active parameters. That distinction matters when interpreting the model’s size and the company’s efficiency claims: the total count is not the same as the number active during a given computation.
Reflection says Beam is intended for coding, reasoning, and agentic tasks—work in which a model may use tools or carry out multi-step actions. These are the company’s stated targets, not a guarantee that Beam will perform equally well across every application.
How does Reflection say Beam was trained?
Reflection says it pretrained Beam on 23.8 trillion curated tokens drawn from web and licensed datasets. The company also reports that its reinforcement-learning run generated more than 100 million rollouts using 10.5K NVIDIA GB300 GPUs over four weeks. These are company disclosures; the announcement does not independently verify the training figures.
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What benchmark results did Reflection report?
Reflection’s published evaluation table includes scores of 80.9 on SWE-bench Verified, 80.1 on Terminal Bench 2.1, and 90.5 on GPQA Diamond. The company presents Beam as competitive with larger open models on coding and agentic work. It also claims comparable advanced-reasoning scores to GLM-5.2 using three to four times less inference compute.
Those figures describe Reflection’s reported evaluations, not independently reproduced results. Benchmark scores can depend on evaluation setup, so the announcement alone does not establish that Beam and another model were tested under directly comparable conditions. Likewise, having 23 billion active parameters does not by itself prove faster responses, lower serving costs, or a particular hardware requirement. The announcement does not provide verified user-facing hardware requirements or serving prices.
Is Beam open source, and can you download it?
Reflection called Beam an open-weight model and planned to release its weights under an Apache 2.0 license later in October 2026. At the time of the October 5 announcement, the company said final red-teaming and evaluations were still underway. It also planned to publish a technical report, model card, and developer artifacts alongside the weights.
Open-weight does not necessarily mean that all parts of a model’s development are open. The announcement promises weights and developer artifacts, but does not say that Reflection will release the training data or training code. It also does not confirm that the planned weights or documentation were subsequently published; a reader checking availability should consult Reflection AI’s official site for current release information.
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What deployment and partner details are known?
Reflection said it planned to launch Beam with distribution partners and integrations for open-source libraries and harnesses, but the announcement did not name partners or specify when those options would be available. It provided no Beam-specific pricing.
Reflection’s company site describes broader offerings that include an API platform and deployments in private cloud, on-premises, air-gapped, and edge environments. Those general company capabilities do not establish that every option is available for Beam.
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What remains unknown about Beam?
- Whether the planned weights, Apache 2.0 license, technical report, model card, and developer artifacts have since been released.
- Beam’s verified hardware requirements, serving costs, and performance in independent benchmark replications.
- Which distribution partners and software integrations will support Beam, and when they will be available.
- Whether Reflection will publish training data or training code; the launch announcement does not promise either.
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