Laguna XS.2 and M.1
The company has released two new models in the Laguna family, Laguna M.1 and Laguna XS.2, designed for agentic coding and long-horizon tasks. Laguna M.1 is a 225B-parameter Mixture of Experts model trained in-house, while Laguna XS.2 is a smaller, open-weight model with 33B total parameters. Both models are available for free use via API and OpenRouter, with XS.2's weights released under an Apache 2.0 license.
- ▪Laguna M.1 is a 225B-parameter Mixture of Experts model with 23B activated parameters, trained on 30T tokens using 6,144 NVIDIA Hopper GPUs.
- ▪Laguna XS.2 is a 33B-parameter MoE model with 3B activated parameters and is the company's first open-weight release under the Apache 2.0 license.
- ▪Laguna M.1 achieved 46.9% on SWE-bench Pro and 40.7% on Terminal-Bench 2.0, while XS.2 reached 44.5% and 30.1% respectively.
- ▪Both models are built for agentic coding and long-horizon tasks, emphasizing code execution as a more expressive interface than tool calling.
- ▪The models were developed by the company's 60-person Applied Research team and are available in preview through API and OpenRouter.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Poolside |
| Canonical URL | https://poolside.ai/blog/laguna-a-deeper-dive |
| Publication time | Tue, 28 Apr 2026 16:17:55 +0000 |
| Retrieval time | 2026-04-28T16:47:47.399Z |
| Last seen | 2026-04-28T16:47:47.399Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Poolside.
Discussion
0 commentsMore from Poolside
