Laguna-XS.2

Laguna XS.2 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. It uses Sliding Window Attention with per-head gating in 30 out of 40 layers for fast inference and low KV cache requirements.

[!NOTE] For more details on how we trained this model, including on data automixing and async off-policy agent RL, check out our release blog post and technical report.

Highlights

• Mixed SWA and global attention layout: Laguna XS.2 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 is compact enough to run on a Mac with 36 GB of RAM. Available on Ollama
• Apache 2.0 license: Use and modify freely for commercial and non-commercial purposes

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

We used the highest publicly-referenced scores for all comparison models across each benchmark. In almost all cases these were official scores published in release blog posts or equivalent, with the exception of Gemma 4 31B IT where the highest published scores were reported by the Qwen team and Claude Haiku 4.5 where the highest published (verified) scores for SWE-bench Pro and Terminal-Bench 2.0 are from their respective official leaderboards.

All benchmarking for Laguna XS.2 was completed using the Laude Institute’s Harbor Framework with our agent harness, using a maximum of 500 steps and sandboxed execution using 8 GB RAM/2 CPUs (with the exception of Terminal-Bench 2.0; see below). The same sampling parameters were used for all benchmarking: temperature=0.7 and top_k=20. Some base task images and verifiers were patched to fix infrastructure reliability issues inherent in task setup, such as rate limits on third-party dependencies in external registries used by the verifier. More details outlining these updates and other findings will follow in a future technical blog post.

• SWE-bench Verified: mean pass@1 averaged over 4 runs.
• SWE-bench Multilingual: mean pass@1 averaged over 7 runs.
• SWE-bench Pro: mean pass@1 averaged over 3 runs.
• Terminal-Bench 2.0: mean pass@1 averaged over 5 runs. 48GB RAM/32 CPUs.

Usage

Laguna XS.2 has launch-day support in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA.

The fastest way to get started is with our API, directly or using OpenRouter.

[!NOTE] We are providing free access for a limited time to Laguna XS.2, and our larger 225B model, Laguna M.1, on our API. You can create an API key on our Platform.

pool

pool is a lightweight terminal-based coding agent and a dual Agent Client Protocol client-server.

Download and install for macOS and Linux:

curl -fsSL https://downloads.poolside.ai/pool/install.sh | bash

Launch and Log in with Poolside to get a free API key.

pool

Use in any ACP client. Configure Zed and JetBrains automatically:

pool acp setup --editor zed|jetbrains

Use pool with Ollama with one-command setup:

ollama pull laguna-xs.2
ollama launch pool --model laguna-xs.2

Feedback and issues

Submit feedback with /feedback and read the full documentation on GitHub.

Local deployment

Laguna XS.2 is supported in vLLM, SGLang, and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna-XS.2 with Ollama (with MLX supp