Reflection AI Introduces Beam: 501B Open-Weight MoE
Reflection AI unveils Beam, a 501B sparse Mixture-of-Experts model with 23B active parameters optimized for coding and agentic tasks.
Stock photo for illustration only, not from the actual event
- Reflection AI introduces its first open-weight model named Beam
- 501B sparse Mixture-of-Experts architecture with 23B active parameters
- Matches GLM-5.2 reasoning with 3 to 4x less inference compute
- Apache 2.0 weights scheduled for release in late October 2026
Reflection AI has officially announced Beam, marking the company's very first open-weight model release. Structured as a sparse Mixture-of-Experts (MoE) model, Beam features a total of 501 billion parameters alongside 23 billion active parameters per computation. The model is specifically engineered to handle complex coding tasks and agentic workloads.
According to the company, Beam achieves reasoning capabilities on par with GLM-5.2 while requiring 3 to 4 times less inference compute. This efficiency highlights a major step forward in optimizing advanced artificial intelligence systems for real-world development.
Stock photo for illustration only, not from the actual event
Mixture-of-Experts (MoE) architectures allow models to scale up total parameters massively while activating only a fraction for each token, striking a balance between high intelligence and lower operational latency. Reflection AI's decision to release the weights under the Apache 2.0 license further signals a commitment to developer accessibility and open research.
The company confirmed that the Apache 2.0 model weights are scheduled to be released later in October 2026.
Source: MarkTechPost
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