Reflection AI Releases Open Weights for Beam, a 501B Model Trained to Cut Token Costs

Reflection AI released the weights for Beam, its first open-source model, on October 16, 2026, under the Apache 2.0 license. The model features 501 billion total parameters and 23 billion active parameters, targeting coding, reasoning, and long-horizon agentic tasks.

Unlike models that prioritize maximum capability, Reflection AI positions Beam as an efficiency-focused alternative. The company states it used large-scale reinforcement learning during training, applying a penalty whenever the model generated unnecessary tokens. According to VentureBeat, Reflection AI CTO Ioannis Antonoglou explained that this mechanism forces the model to solve tasks while remaining concise, thereby reducing the number of tokens required to finish each job.

This strategy aims to compete with Chinese open models like DeepSeek on cost. While Beam's absolute performance on benchmarks such as Terminal-Bench 2.1 does not yet surpass top Chinese models, Reflection AI argues that enterprises care more about the total cost of completing a task (price per token multiplied by tokens used) than just the per-token price or peak capability.

Beam's weights are now available on Hugging Face in BF16, FP8, and NVFP4 formats, with day-zero support from major inference frameworks including vLLM, SGLang, and Ollama.