Laguna XS.2 and M.1

2026-04-28 Laguna XS.2 and M.1: A Deeper Dive Poolside team Table of contents We’ve released the first two models in the Laguna family, Laguna M.1 and Laguna XS.2, alongside the runtime we use to train and operate agents, available through two product experiences in preview.Open weightsComing soonWorking with NVIDIAModel buildingData and automixingMuonAgent RLGet started We’ve released the first two models in the Laguna family, Laguna M.1 and Laguna XS.2, alongside the runtime we use to train and operate agents, available through two product experiences in preview.Laguna M.1 came first, finishing pre-training at the end of last year; it's the foundation for everything else we're building across the family. Laguna XS.2 is a much smaller model, but remarkably capable for its size, and it's our first open-weight release. Both models are free to use for a limited time via our API and on OpenRouter, and Laguna XS.2 weights are also available under an Apache 2.0 license.Laguna XS.2 and Laguna M.1 are agentic coding models built for long-horizon work. To date, we’ve been focused on serving our government and public sector clients with capable models deployable into the highest-security environments. And while our commitment to these customers remains, we’re now ready to share where we are with the world. We’re also excited to release the weights of Laguna XS.2, our newest generation model, to the open ecosystem to support builders and the wider research community.We're working toward models that enable more capable agents; and we believe the path runs through coding capability and increasingly long-horizon tasks. Creating software is the core skill through which many other capabilities get expressed.Today, most agents interact with the world through tool calling, where structured interfaces restrict agents to a fixed set of actions defined in advance. We think this is a transitional pattern. Software is a much more expressive interface. An agent that can write and execute code can compose actions, parallelize work, and build its own ad-hoc systems to interact with the world.These models are the work of the roughly 60 people who make up our Applied Research organization, across architecture, data, pre-training, and reinforcement learning. We're excited to bring this work into the world and see what the community builds with it. Laguna M.1 225B-A23B Laguna XS.2 33B-A3B Qwen3.5 397B-A17B Qwen3.5 35B-A3B Qwen3.6 35B-A3B Claude Sonnet 4.6 - Laguna M.1 is our most capable model to date and completed pre-training at the end of last year. It's a 225B total parameter Mixture of Experts (MoE) model with 23B activated parameters, trained completely in-house and from scratch on 30T tokens, using 6,144 interconnected NVIDIA Hopper GPUs. Laguna M.1 reaches 46.9% on SWE-bench Pro and 40.7% on Terminal-Bench 2.0. Laguna M.1 225B-A23B Devstral 2 123B dense† GLM-4.7 355B-A32B DeepSeek-V4-Flash 284B-A13B Qwen3.5 397B-A17B Claude Sonnet 4.6 - † We have chosen to include dense models with larger activated parameter counts to highlight the relative efficiency of MoE models.Laguna XS.2 is our second-generation MoE and our first open-weight model, built on everything we've learned since training Laguna M.1 across data, including synthetic, and RL. At 33B total parameters with 3B activated (30T tokens trained), it's a highly capable open-weight agentic coding model in its weight class, reaching 44.5% on SWE-bench Pro and 30.1% on Terminal-Bench 2.0. The weights are…

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