Laguna-XS-2.1

Use on OpenRouter · Release blog post Laguna XS 2.1 Laguna XS 2.1 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. This model is an upgraded version of our Laguna XS.2 model with a +5.4% jump on SWE-bench Multilingual as well as stronger performance on terminal-style tasks. [!NOTE] For more details on how we train, including on data automixing and async off-policy agent RL, check out our recent technical report. Highlights **Mixed SWA and global attention layout**: Laguna XS 2.1 uses sigmoid gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 40 total layers) **KV cache in FP8**: KV cache quantized to FP8, reducing memory per token **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request **Local-ready**: At 33B total parameters and 3B activated, Laguna XS 2.1 is compact enough to run on a Mac with 36 GB of RAM. Available on Ollama and llama.cpp. High-quality FP8, NVFP4 and INT4 quantized variants available (see the collection) **OpenMDW-1.1 license**: Use and modify the model and associated materials freely for commercial and non-commercial purposes (learn more about OpenMDW) Model overview Training: pre-training, post-training and reinforcement learning stages Number of parameters: 33B total with 3B activated per token Optimizer: Muon Layers: 40 layers (10 layers with global attention, 30 layers with sliding window attention) Experts: 256 experts with 1 shared expert Sliding Window: 512 tokens Modality: text-to-text Context window: 262,144 tokens Reasoning support: interleaved thinking with preserved thinking Benchmark results | Model | Size (total params.) | SWE-bench Verified | SWE-bench Multilingual | SWE-Bench Pro (Public Dataset) | Terminal-Bench 2.0 | |---------------------------|------------------