❯ Reflection unveils its first open-weight model, Beam, saying it needs a third to a quarter of GLM-5.2’s inference compute

501 billion parameters, 23 billion activeNvidia-backed Reflection AI unveiled its first open-weight model, Beam, on October 5. According to MarkTechPost, it uses a sparse mixture-of-experts architecture with 501 billion total parameters and only 23 billion active at a time, targets coding, reasoning and agentic tasks, and supports a context of up to 1 million tokens. Full weights are due in late October under Apache 2.0, with an early version available through a waitlist.

Trained in four weeksIn a mixture-of-experts design the model contains many expert modules and calls only a few for each answer, so total parameters are large while actual computation is small. Reflection says Beam was pretrained in under four weeks on 6,144 Nvidia GB300 GPUs with 23.8 trillion tokens. The reinforcement learning stage ran more than 100 million rollouts and used about 1.3 billion sandboxes.

Claims to approach China's leading open modelsReflection says Beam is comparable to GLM-5.2 on most tasks and approaches Qwen 3.8-Max on coding and agentic tasks, while acknowledging that Kimi K3 has stronger raw capability. The claim of three to four times less inference compute is, by the company’s own description, an approximate compute comparison and not a measured inference cost, leaving out prompt prefill and serving overhead. Benchmarking firm Artificial Analysis says it has been given access and is testing independently.

Where American open models standResearcher Nathan Lambert commented on X that Reflection joins Nvidia and Thinking Machines in releasing its strongest model and coming up behind Chinese counterparts, and that the clearest takeaway is that Chinese teams are very good at building LLMs. Companies that want to run models on their own servers gain an option from a US company, but whether it really saves compute awaits third-party checks once the weights are out.

▮ SIGNALAmerican makers of open models no longer claim to beat their Chinese peers and instead pitch more efficiency at equal capability, a quiet change in the frame of reference.