Poolside’s Laguna S 2.1 is a small open-weight coding model that punches well above its size
Poolside has released Laguna S 2.1, its third coding model in three months. The mixture-of-experts model uses 8 billion active parameters and focuses less on raw scale than on better behavior during long agentic sessions.
US-based Poolside says Laguna S 2.1 outperforms other agentic coding models in its weight class and sometimes approaches systems 10 to 20 times its size. The model has 118 billion total parameters, with 8 billion active for each token. It supports context windows of up to one million tokens and offers thinking and no-thinking modes. Poolside made its first models available to a broader audience in April 2026 with Laguna M.1 and XS.2. Until then, the company had focused on government and public-sector customers. XS.2 was also its first open model under the Apache 2.0 license. Laguna S 2.1 is the third version in the series released in roughly three months. With thinking enabled, Laguna S 2.1 scores 70.2 percent on Terminal-Bench 2.1, which tests models on long-running terminal tasks. It ranks just behind Tencent’s Hy3 (295B-A21B) and ahead of much larger open models, including DeepSeek-V4-Pro-Max, Nemotron 3 Ultra, and Thinking Machines Lab’s debut model. The overall leaderboard is led by OpenAI’s GPT-5.6 Sol, Anthropic’s Claude Fable 5, and Kimi K3. Poolside says Datacurve’s DeepSWE benchmark offers a better comparison because its scores are spread across a wider range. Laguna S 2.1 scores 40.4 percent, while some open-weight models with more than one trillion parameters remain below 10 percent. It also ranks near the top of its class on SWE-Bench Multilingual, SWE-Bench Pro, and SWE Atlas. Thinking mode has a major impact on performance. Without it, Laguna S 2.1’s Terminal-Bench score drops to 60.4 percent, while its DeepSWE score falls to 16.5 percent.
