Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads
Reflection AI unveiled Beam, a 501B open-weight MoE with 23B active parameters for coding and agents.
Reflection AI introduced Beam, its first open-weight model, a 501-billion-parameter sparse Mixture-of-Experts system with 23 billion active parameters aimed at coding and agentic work. The company says Beam matches GLM-5.2 on reasoning while using three to four times less inference compute. Apache 2.0 weights are expected later in October 2026.
- Beam is Reflection AI's first open-weight model.
- It is a 501B sparse MoE with 23B active parameters.
- Reflection says it matches GLM-5.2 using 3 to 4 times less inference compute.
- Apache 2.0 weights are scheduled for later in October 2026.
- The model targets coding and agentic workloads.
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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