Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual
MarkTechPost Asif Razzaq
Poolside released Laguna S 2.1, a 118B-parameter open-weight coding model that uses sparse mixture-of-experts to activate only 8B parameters per token while maintaining full model size in memory. The model scores 78.5% on SWE-Bench Multilingual, leading all published open models, and 70.2% on Terminal-Bench 2.1 with thinking enabled, outperforming much larger systems like DeepSeek-V4-Pro-Max and NVIDIA Nemotron 3 Ultra. At 4-bit quantization the model fits on a single NVIDIA DGX Spark with 128 GB memory, making it deployable on single-GPU hardware while competing with models several times its parameter size.
Why it matters
Poolside has released Laguna S 2.1, a 118B open-weight Mixture-of-Experts coding model with 8B active parameters per token and a 1M-token context. It matches or beats models several times its size on agentic coding benchmarks, ships under OpenMDW-1.1, and runs on a single NVIDIA DGX Spark. The post Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual appeared first on MarkTechPost.
