Laguna M.1/XS.2 Technical Report
About
We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has $225.8$B total parameters ($23.4$B activated per token) and XS.2 has $33.4$B total ($3$B activated). Both models were trained from scratch end-to-end inside the same internal system that we refer to as our Model Factory: a tightly-integrated stack of versioned data, training, evaluation, and inference components that turn model development into an industrial process. We describe the principles and design choices of the Model Factory and also detail the end-to-end training process of our models, throughout pre-training data and architecture, post-training stages, evaluation, and quantization. On agentic software engineering and terminal benchmarks (SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro, and Terminal-Bench 2.0) M.1 and XS.2 are competitive with state-of-the-art open models in their respective weight classes. Laguna XS.2 weights are released under Apache~2.0 at https://huggingface.co/collections/poolside/laguna-xs2.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| General Reasoning | BBH | BBH General Reasoning Accuracy80.9 | 103 | |
| Code Output Prediction | CRUXEval-O | Pass@171.7 | 52 | |
| Code Input Prediction | CRUXEval-I | Pass@161.9 | 52 | |
| Agentic Coding | Terminal-bench 2.0 | Pass@145.8 | 18 | |
| Agentic Coding | SWE-bench Verified | Pass@174.6 | 17 | |
| Multi-task Language Understanding | MMLU STEM | Accuracy78.1 | 13 | |
| Agentic Coding | SWE-Bench Multilingual | Accuracy57.7 | 13 | |
| Language Understanding | MMLU-Pro | Exact Match53 | 11 | |
| Agentic Coding | SWE-Bench Pro | Pass@149.2 | 9 | |
| Code Generation | MultiPL-E | Pass@158.4 | 5 |