Laguna-S-2.1

Use on OpenRouter · Use on Vercel AI Gateway · Release blog post Laguna S 2.1 Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, designed for agentic coding and long-horizon work. It sits between Laguna XS 2.1 (33B-A3B) and Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a token-choice router with softplus gating over 256 routed experts plus one shared expert, grouped-query attention, and interleaved full/sliding-window attention. Highlights **Mixed SWA and global attention layout**: 48 layers in a 1:3 global-to-SWA ratio (12 global attention layers, 36 sliding-window layers, window 512), with softplus attention gating and per-layer-type rotary scales **1M context**: 1,048,576-token context window **Native reasoning support**: interleaved thinking between tool calls, with per-request control via `enable_thinking` **Speculative decoding**: a trained DFlash draft model is available for lower-latency serving **Quantized variants**: FP8, NVFP4, INT4 and GGUF **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 Number of parameters: 118B total, ~8B activated per token Layers: 48 (12 global attention, 36 sliding-window attention) Experts: 256 routed (top-10) plus 1 shared expert Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating Sliding window: 512 tokens Context window: 1,048,576 tokens Vocabulary: 100,352 tokens (Laguna family tokenizer) Modality: text-to-text Reasoning: interleaved thinking with preserved thinking Benchmark results | Model | Size | Terminal-Bench 2.1 | SWE-bench Multilingual | SWE-Bench Pro (Public Dataset) | DeepSWE | SWE Atlas (Codebase QnA) | Toolathlon Verified | |---|---|---|---|---|---|---|---| | **Laguna S 2.1** | 118B-A8B | **70.2%** | **78.5%** | **59.4%** | **40.4%** | **46.2%** | **49.7%** | | Tencent Hy3 | 295B-A21B |