tl;dr: Poolside released Laguna S 2.1, a 118B-parameter MoE model with 8B active parameters and a 1M token context window, trained in under nine weeks and targeting long-horizon agentic coding. It scores 70.2% on Terminal-Bench 2.1 and 40.4% on DeepSWE, competitive with models many times its size, and independently rediscovered a proof to Erdős problem #397. Weights are available on Hugging Face under OpenMDW-1.1 with BF16/FP8/INT4/NVFP4 variants, and the company is publishing full evaluation trajectories to address reward-hacking concerns.