[whatmodel.sh](../../README.md) / Laguna XS.2 # Laguna XS.2 Poolside · released 2026-04 · 33B params (3B active) · apache-2.0 **score 63.6** ±1.4 · #161 of 732 · from 8 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Laguna XS.2 among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  ●···█······█       #3 of 3     63%      4
coding       ▁▁▁▃▅▇█▇●▄▃▁   #136 of 469     81%      4
agent tasks · #3 of 3 · 63% of its score 
test                            result  vs the others in its run   difficulty           counts  why
SWE-bench Verified (maker)       69.9%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 79/138  hard · avg 28%          23%  sharp · at its level · small run · seve…
Terminal-Bench 2.0 (maker)       35.7%  ▂▂▅▅●███▆▂▃▃ beats 8/36    very hard · avg 12%     18%  at its level · small run · several runs
SWE-bench Multilingual (maker)   57.7%  ▁··▁▃▁●██▂▂▁ beats 20/65   hard · avg 29%          18%  at its level · small run · several runs
SWE-bench Pro (maker)            46.3%  ●▂▂▂▄▂▆█·▂·▂ beats 1/16    hard · avg 30%         3.4%  blunt · at its level · small run
coding · #136 of 469 · 81% of its score Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Gemma 4 12B](gemma-4-12b.md), [Seed-OSS 36B Instruct](seed-oss-36b.md) here, though they rank lower overall. 
test                            result  vs the others in its run   difficulty      counts  why
Arena WebDev (Code Arena)       1302.4  ▁▁▃●▅█▃▆▅▅▁▂ beats 21/129  avg 981            36%  sharp
SWE-bench Verified (maker)       69.9%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 79/138  hard · avg 28%     23%  sharp · at its level · small run · seve…
SWE-bench Multilingual (maker)   57.7%  ▁··▁▃▁●██▂▂▁ beats 20/65   hard · avg 29%     18%  at its level · small run · several runs
SWE-bench Pro (maker)            46.3%  ●▂▂▂▄▂▆█·▂·▂ beats 1/16    hard · avg 30%    3.4%  blunt · at its level · small run
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
LMArena leaderboard dataset                     1   36% ▇▇▇▇
Poolside vendor-reported benchmark scor…        4   35% ▇▇▇▇
Poolside (submitted / maker-reported)           3   28% ▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 63.6
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Poolside ([CC BY-SA 4.0](../../licenses/report-2605.27605v1.txt))
Not counted (1) 
source                  test           score  why
poolside/Laguna-XS.2 …  SWE-bench Pro  46.3%  only model in this source's run
## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Laguna XS.2 (Q4_K_M, needs ~20.3 GB, from [huggingface.co/Lucebox](https://huggingface.co/Lucebox/Laguna-XS.2-GGUF/tree/d0be991dc6857beb4ea04051db998cc539c49d99)) ```sh llama cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M ``` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Laguna XS.2 (Q4_K_M, needs ~20.3 GB, from [huggingface.co/Lucebox](https://huggingface.co/Lucebox/Laguna-XS.2-GGUF/tree/d0be991dc6857beb4ea04051db998cc539c49d99)) ```sh lms get https://huggingface.co/Lucebox/Laguna-XS.2-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 4bit (~18.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Laguna-XS.2-4bit -y` · 8bit (~35.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Laguna-XS.2-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Laguna XS.2 (Q4_K_M, needs ~23 GB, from [ollama.com](https://ollama.com/library/laguna-xs.2/tags)) ```sh ollama run laguna-xs.2:latest ``` Other versions: Q4_K_M (~23 GB) `ollama run laguna-xs.2:latest` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Laguna XS.2 (4bit, needs ~18.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Laguna-XS.2-4bit/tree/799182fa9455de7af14137b5033f941a3f71450c)) ```sh mlx_lm.chat --model mlx-community/Laguna-XS.2-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Laguna-XS.2-4bit ``` Other versions: 8bit (~35.5 GB, Mac only) `mlx_lm.chat --model mlx-community/Laguna-XS.2-8bit` ### Other ways to run it 1. Run it as published (BF16, needs 66.89 GB, NVIDIA only, from [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-XS.2/tree/a397bde04501591d0ac3d74a5d9d0a4ec298ffb8)) ```sh vllm serve poolside/Laguna-XS.2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice ``` Model repository and documentation: [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-XS.2) [interactive version](https://whatmodel.sh/model/laguna-xs-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)