Poolside's Laguna S 2.1 Beats 1.6T Models at Coding on a Single Desktop

Laguna S 2.1 is Poolside's biggest open-weight model release to date: a 118 billion parameter Mixture-of-Experts (MoE) model that activates only 8 billion parameters per token, supports a 1 million token context window, and is purpose-built for long-horizon agentic coding. The weights are live on Hugging Face under the permissive OpenMDW-1.1 license, free to download and use commercially.

Laguna S 2.1 went from the start of training to launch in under nine weeks, and on long-horizon coding benchmarks it holds its own against models many times its size. The headline claim is simple: the 118-billion-parameter model matches models several times its size on agentic coding and is small enough to run on a single desktop.

Numbers that matter

Poolside ran Laguna S 2.1 across six agentic coding benchmarks. The results are striking for a model this size:

• Terminal-Bench 2.1: 70.2% -- ahead of DeepSeek-V4-Pro Max (1.6T params, 64.0%), NVIDIA Nemotron 3 Ultra (550B, 56.4%), and Thinking Machines' Inkling (975B, 63.8%)
• SWE-Bench Multilingual: 78.5% -- beating Tencent Hy3 (295B, 75.8%) and Nemotron 3 Ultra (67.7%)
• SWE-Bench Pro: 59.4% -- ahead of DeepSeek-V4-Pro Max (55.4%) and Inkling (54.3%)
• DeepSWE v1.1: 40.4% -- outperforming DeepSeek-V4-Pro Max which scored just 9.0% on this benchmark

DeepSWE still has significant headroom. Its tasks are longer-horizon and hard to partially solve, and the scores actually spread: frontier models range from 54% to 73% on the v1.1 variant, with some 1T+ parameter open models scoring below 10%. That spread makes it a more meaningful signal than saturated benchmarks where top models cluster within a few points of each other.

Thinking mode matters a lot here. Max thinking lifts S 2.1's score on Terminal-Bench 2.1 from 60.4% to 70.2% and on DeepSWE from 16.5% to 40.4%. The tradeoff is token cost: thinking mode uses roughly 129K tokens per trajectory on Terminal-Bench versus 80K without it.

What "agentic" actually means here

Poolside published three unedited case studies showing the model working autonomously. They're worth reading to understand what the benchmarks are actually measuring.