Reflection's Beam: 501B Parameters, 23B Active

🔥 What happened Reflection unveiled Beam, its first open-weight model: a sparse Mixture-of-Experts with 501B total parameters but only 23B active per token. It was pretrained on 23.8 trillion tokens and then put through a massive RL run — over 100 million rollouts on 10,500 NVIDIA GB300 GPUs for four weeks. 💡 Why it matters Beam reportedly matches GLM 5.2 on coding and agentic benchmarks while using 3–4× less inference compute. That's the real story: not raw capability, but intelligence per token. For enterprise workloads, that could slash cost per task — and it puts pressure on labs still scaling dense models. ⚡ Our take Reflection isn't playing the parameter arms race; it's playing the efficiency game — and that's where the actual edge is right now. If the weights deliver, Beam becomes the workhorse for coding agents.