Reflection AI announced Beam on October 5, 2026, as its first open-weight model: a sparse Mixture-of-Experts system with 501 billion total parameters and 23 billion active parameters. Reflection says it is designed for coding, reasoning and agentic workloads. The announcement describes a planned release, not confirmed public availability: at launch, final red-teaming and evaluations were still underway.
What is Reflection AI’s Beam model?
Beam is a sparse Mixture-of-Experts (MoE) model. Reflection reports 501 billion total parameters, of which 23 billion are active for a given computation. That makes it different from a conventional dense model in which all parameters are used for every token. The active-parameter figure is relevant to efficiency, but it does not by itself establish how quickly Beam runs, what hardware users need, or what serving it costs.
Reflection positions Beam for coding, reasoning and agentic tasks—workflows in which a model may use tools or take steps toward a goal. The company calls it its first open-weight model. Its announcement says weights and developer materials were planned for release, but does not say that training data or training code would be released. Calling Beam “open source” without that distinction would overstate what the announcement establishes.
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 a reinforcement-learning run that generated more than 100 million rollouts using 10.5K NVIDIA GB300 GPUs over four weeks. These are Reflection’s disclosures; the announcement does not provide independent verification of the figures or release training code and data.
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What benchmark results did Reflection publish?
Reflection’s evaluation table reports scores including 80.9 on SWE-bench Verified, 80.1 on Terminal Bench 2.1 and 90.5 on GPQA Diamond. The company says Beam is competitive with larger open models on coding and agentic work. It also claims advanced-reasoning scores comparable to GLM-5.2 with three to four times less inference compute.
These are vendor-reported results and comparisons, not independently reproduced findings. Benchmark outcomes can depend on evaluation setups, prompting, tools and other conditions; the announcement does not establish that every comparison uses equivalent setups. Reflection also describes caveats in how it estimates compute for the GLM-5.2 comparison, so the claimed reduction should not be read as a general measure of user-facing speed or cost. Without independent replication and confirmed serving details, the table is evidence of what Reflection reports—not a guarantee of performance in a particular deployment.
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Is Beam available to download, and what is its license?
At the October 5 announcement, Reflection said Beam was undergoing final red-teaming and evaluations. It planned to release the weights under an Apache 2.0 license later in October 2026, alongside a technical report, model card and developer artifacts. The announcement is a plan, not confirmation that those materials were subsequently published. Check Reflection’s official announcement and release channels for current availability before treating the weights or documentation as downloadable.
Reflection also said it planned distribution partners and integrations with open-source libraries and harnesses, but did not name partners, give availability dates or specify pricing. Its website describes broader company offerings such as an API platform and private-cloud, on-premises, air-gapped and edge deployments. Those company-wide options do not establish that each is available for Beam.
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What hardware does Beam need?
The announcement does not state Beam’s user-facing hardware requirements, a supported configuration, or a serving price. The 10.5K NVIDIA GB300 GPUs cited by Reflection were used in its reported reinforcement-learning campaign; that training figure is not a hardware recommendation for running the model. Likewise, 23 billion active parameters alone cannot determine the memory, latency or infrastructure needed for a particular deployment.
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