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Poolside's Laguna S 2.1 demonstrates that efficient Mixture-of-Experts architectures can deliver agentic coding performance comparable to much larger closed systems while remaining deployable on single-node infrastructure like the NVIDIA DGX Spark. The model achieves this by activating only a fraction of its parameters per token, reducing inference costs without sacrificing capability on long-horizon benchmarks such as SWE-Bench Multilingual and Terminal-Bench 2.1. This release highlights significant progress in making high-performance coding agents accessible for practical deployment rather than just research evaluation.

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Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual

MarkTechPostJuly 22, 20265 min read

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.

Poolside has released Laguna S 2.1, a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model is small enough to run on a single NVIDIA DGX Spark. On long-horizon coding benchmarks, Laguna S 2.1 holds its own against models several times its size, including DeepSeek-V4-Pro-Max, NVIDIA’s…

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