Reflection AI Beam Model Uses 23B Active Parameters for Efficient AI Inference

Mirror Review

October 6, 2026

The Reflection AI Beam model is a 501-billion-parameter open-weight AI model with 23 billion active parameters, designed for coding, reasoning, and agentic workloads. Reflection AI introduced Beam on October 5, 2026, and reported that the model is competitive with GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks.

The Beam AI model uses a sparse Mixture-of-Experts architecture, with 23 billion of its 501 billion parameters active during processing. Reflection AI estimates that Beam uses three to four times less inference compute than GLM 5.2 on the cited reasoning comparisons.

What Is the Reflection AI Beam Model?

The Reflection AI Beam model contains 501 billion total parameters and 23 billion active parameters in its sparse Mixture-of-Experts architecture.

Reflection AI pretrained Beam on 23.8 trillion tokens and used large-scale reinforcement learning for coding, reasoning, and tool-based tasks.

Why Is Beam Focused on Inference Efficiency?

Reflection AI reports that Beam achieves comparable scores to GLM 5.2 on advanced reasoning benchmarks while using an estimated three to four times less inference compute.

Reflection AI calculates its Beam inference compute estimate from forward-pass computation and generated tokens. Reflection AI says the estimate excludes prompt prefill, context-dependent attention operations, and serving overhead, making it an approximate compute comparison rather than a measured inference-cost figure.

How Does Beam Compare With GLM 5.2 and Qwen 3.8-Max?

For Beam vs GLM-5.2, Reflection AI reports that Beam achieves comparable scores to GLM 5.2 on the cited advanced reasoning benchmarks while using an estimated three to four times less inference compute.

For Beam vs Qwen3.8-Max, Reflection AI says Beam approaches Qwen 3.8-Max on coding and agentic workloads.

For Beam vs Kimi K3, Reflection AI says Kimi K3 remains ahead on raw capability, while Reflection AI positions Beam around inference efficiency.

Reflection AI reports scores of 80.9 on SWE-bench Verified and 80.1 on Terminal Bench v2.1. The figures are company-reported benchmark results rather than independent validation.

What Does Reflection AI’s Open-Weight Model Offer?

Reflection AI plans to release Beam’s model weights for developers and organizations that want to customize and run the model. Reflection AI plans to release Beam’s weights, technical report, model card, and developer artifacts later in October under an Apache 2.0 license.

The current Beam release is still undergoing final red-teaming and evaluations. Reflection AI has made an early version available to selected users while the wider release remains scheduled for later in October.

How Does Reflection AI Beam Compare With Chinese Open-Weight AI Models?

Reflection AI positions Beam against Chinese open-weight models including GLM 5.2 and Qwen 3.8-Max. Reflection AI reports that Beam is competitive with GLM 5.2 and approaches Qwen 3.8-Max on coding and agentic workloads.

What Is the NVIDIA and Reflection AI Connection?

Reflection AI used NVIDIA GB300 GPUs to train Beam and run reinforcement-learning workloads, highlighting the role of NVIDIA AI infrastructure in large-scale model development.

Reflection AI says Beam’s reinforcement-learning run used 10,500 NVIDIA GB300 GPUs for four weeks and generated more than 100 million rollouts. Reflection AI says Beam used a maximum 256K-token context during reinforcement learning.

Who Is the Reflection AI Beam Model Designed For?

Reflection AI says Beam is intended for developers, enterprises, and public-sector organizations working with enterprise AI, with coding, reasoning, and agentic workloads among its primary use cases.

Reflection AI says its open-weight approach allows organizations to deploy and customize Beam on their own infrastructure.

What Happens Next for the Reflection AI Beam Model?

The Reflection AI Beam model is undergoing final red-teaming and evaluation, with early access available to selected users. Reflection AI plans to release Beam’s model weights, technical report, model card, and developer artifacts later in October under an Apache 2.0 license.

The wider release will allow developers and organizations to test and customize the Reflection AI Beam model with access to its model weights. Reflection AI’s reported inference-compute results and benchmark scores can then be evaluated more broadly as Beam becomes available.

Gurushanth S Jatti