Poolside releases Laguna XS 2.1: 33B parameter MoE coding model with 262K context window
Poolside has released Laguna XS 2.1, a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token and a 262,144-token context window. The model achieves 70.9% on SWE-bench Verified and 63.1% on SWE-bench Multilingual, representing a 5.4% improvement over its predecessor on multilingual coding tasks.
Laguna XS 2.1 — Quick Specs
Poolside releases Laguna XS 2.1: 33B parameter MoE coding model with 262K context window
Poolside has released Laguna XS 2.1, a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token and a 262,144-token context window. The model achieves 70.9% on SWE-bench Verified and 63.1% on SWE-bench Multilingual, representing a 5.4% improvement over its predecessor on multilingual coding tasks.
Model specifications
Laguna XS 2.1 uses a 256-expert architecture with 1 shared expert across 40 layers in a 3:1 ratio of sliding window attention (512-token window) to global attention layers. The model employs sigmoid gating with per-layer rotary scales and quantizes its KV cache to FP8 to reduce memory usage. According to Poolside, the model can run on a Mac with 36GB of RAM.
The model was trained using pre-training, post-training, and reinforcement learning stages with the Muon optimizer. It supports native reasoning with interleaved thinking between tool calls that can be enabled or disabled per request.
Benchmark performance
On SWE-bench Verified, Laguna XS 2.1 scored 70.9%, up from 69.9% in version XS.2. The model achieved 63.1% on SWE-bench Multilingual (versus 57.7% previously), 47.6% on SWE-Bench Pro, and 37.5% on Terminal-Bench 2.0.
For comparison, Qwen3.6-35B-A3B scored 73.4% on SWE-bench Verified and 67.2% on SWE-bench Multilingual, while Claude Haiku 4.5 achieved 73.3% on SWE-bench Verified. All benchmarks were run using the Harbor Framework with temperature=1.0, top_k=20, top_p=1, and thinking mode enabled at 256K context length.
Availability and deployment
Laguna XS 2.1 is available with free inference on OpenRouter for a limited time. The model has day-one support in vLLM (version 0.21.0+), SGLang, Transformers (v5.7.0+), Llama.cpp, and TensorRT-LLM (v1.3.0rc16+).
Poolside offers optional speculative decoding through DFlash, a 5-layer draft model that proposes up to 7 tokens per step with approximately 70% per-position acceptance on coding tasks, according to the company.
The model is released under the OpenMDW-1.1 license, which permits commercial and non-commercial use and modification. Poolside also provides a terminal-based coding agent called "pool" with Agent Client Protocol support for macOS and Linux.
What this means
Laguna XS 2.1 targets the growing space of local coding agents, offering competitive SWE-bench scores in a package small enough for consumer hardware. The 5.4% improvement on multilingual coding tasks suggests focused optimization for non-English programming scenarios. However, the model trails larger competitors like MAI-Code-1-Flash (137B parameters) which scores 71.6% on SWE-bench Verified and 65.5% on Multilingual, indicating that parameter efficiency comes with performance tradeoffs on certain benchmarks.
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