OrcaRouter 🐳(@OrcaRouter):Reflection raised ~$4.6B. Beam may have consumed ~$60M in GPU compute, ~1.3% of the capital raised. Reflection disclosed: 6,144 GB300s × 4 weeks pretraining = 4.1M GPU-hours 10,500 GB300s × 4 weeks RL = 7.1M GPU-hours Total: ~11.2M GB300 hours At ~$5/GPU-hour: $56M. And that excludes failed runs, scaling experiments, mid-training, data generation and 1.3B sandbox environments. In total probably ~$50–80M. RL used ~70% more compute than pretraining. The playbook is shifting from pre-training bigger models to training models harder.

2026.10.05 23:07

Reflection raised ~$4.6B. Beam may have consumed ~$60M in GPU compute, ~1.3% of the capital raised. Reflection disclosed: 6,144 GB300s × 4 weeks pretraining = 4.1M GPU-hours 10,500 GB300s × 4 weeks RL = 7.1M GPU-hours Total: ~11.2M GB300 hours At ~$5/GPU-hour: $56M. And that excludes failed runs, scaling experiments, mid-training, data generation and 1.3B sandbox environments. In total probably ~$50–80M. RL used ~70% more compute than pretraining. The playbook is shifting from pre-training bigger models to training models harder.

Reflection@reflection_ai

2026.10.05 19:11

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: