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Laguna XS.2 and M.1

poolside.ai

April 28, 2026

16 min read

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49/100

Summary

Laguna M.1 and Laguna XS.2 are the first two models in the Laguna family, released alongside a runtime for training and operating agents. Open weights will be available soon, with collaboration from NVIDIA for model building, data, automixing, and agent reinforcement learning.

Key Takeaways

Laguna M.1 is a 225 billion parameter Mixture of Experts model that completed pre-training at the end of last year, achieving 46.9% on SWE-bench Pro and 40.7% on Terminal-Bench 2.0.
Laguna XS.2 is the first open-weight model in the Laguna family, featuring 33 billion total parameters and reaching 44.5% on SWE-bench Pro and 30.1% on Terminal-Bench 2.0, with weights available under an Apache 2.0 license.
Both Laguna M.1 and Laguna XS.2 are designed for agentic coding tasks and are available for free use through an API and OpenRouter during a limited preview period.
The models were developed by a team of approximately 60 researchers focusing on architecture, data, pre-training, and reinforcement learning.

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Community Sentiment

Mixed

Positives

The emergence of Laguna XS.2 and M.1 showcases competitive innovation in the AI space, indicating a healthy evolution of local LLMs.
Quantization techniques are making smaller models increasingly viable on consumer hardware, which could democratize access to advanced AI capabilities.
Despite some concerns, the ongoing development in local LLMs suggests exciting potential for future advancements and applications.

Concerns

Laguna XS.2 and M.1 are perceived as underperforming compared to Qwen3.6, raising questions about their competitiveness in the current landscape.
The disparity in benchmark scores between Laguna models and Qwen3.6 indicates that they may not meet the expectations for performance in their class.
There are concerns about the fragmentation of AI research efforts among smaller companies, which could dilute resources and hinder progress.