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Reflection AI's new Beam model represents a significant architectural shift by utilizing a massive 501 billion parameter sparse MoE structure with only 23 billion active parameters, specifically optimized for coding and agentic tasks. This design aims to match the reasoning capabilities of GLM-5.2 while reducing inference compute requirements by three to four times, offering a potential efficiency breakthrough for developers. The release marks Reflection AI's entry into the open-weight ecosystem, though full Apache 2.0 licensing is scheduled for October 2026.

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Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads

MarkTechPostOctober 5, 20261 min read

Reflection AI has introduced Beam, its first open-weight model. It is a 501B sparse Mixture-of-Experts model with 23B active parameters, built for coding and agentic work. Reflection says it matches GLM-5.2 on reasoning with 3 to 4x less inference compute. Apache 2.0 weights are due later in October 2026. The post Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads appeared first on MarkTechPost.

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