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Reflection releases 501B parameter open-weight model Beam with 1M context

Reflection has released its new open-weight model, Beam, featuring a sparse mixture-of-experts architecture with 501 billion total parameters and 23 billion active parameters per token. The model boasts a 1 million token context window and is released under the permissive Apache 2.0 license, allowing for commercial use without restrictive clauses. While Beam offers significant potential for self-hosting, particularly for coding agents due to its claimed token efficiency, users should be aware that its memory requirements are substantial, necessitating multi-GPU setups, and benchmarks are currently self-reported. AI

IMPACT Sets a new standard for open-weight models with large context windows and permissive licensing, potentially enabling more self-hosted AI applications.

RANK_REASON Frontier-lab model release with system card and permissive license. [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reflection releases 501B parameter open-weight model Beam with 1M context

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42 / 100
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Significant
Frontier-lab model release with system card and permissive license. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
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model release, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Clydara ·

    Reflection's Beam is open: 23B active, but you still have to fit 501B

    <p>Reflection released the weights for Beam on October 16, 2026. It is the lab's first open-weight model: a sparse mixture-of-experts (MoE) model with 501B total parameters, about 23B active per token, a 1M-token context window, and an Apache 2.0 license.</p> <p>Most posts stoppe…