[whatmodel.sh](../../README.md) / Laguna XS 2.1 (33B-A3B)
# Laguna XS 2.1 (33B-A3B)
Poolside · released 2026-06 · 33B params (3B active) · openmdw-1.1
**score 64.4** ±2.4 · #150 of 732 · from 4 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Laguna XS 2.1 (33B-A3B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks █···●······█ #2 of 3 98% 4
coding ▁▁▁▃▅▇█▇●▄▃▁ #117 of 469 71% 3
agent tasks · #2 of 3 · 98% of its score
test result vs the others in its run difficulty counts why
SWE-bench Verified (maker) 70.9% ▁▁▁▁▁▁▂▄●▆▁▁ beats 86/138 hard · avg 28% 33% sharp · at its level · small run
SWE-bench Multilingual (maker) 63.1% ▁··▁▃▁▅●█▂▂▁ beats 26/65 hard · avg 29% 29% at its level · small run
Terminal-Bench 2.0 (maker) 37.5% ▂▂▅▅●███▆▂▃▃ beats 11/36 very hard · avg 12% 26% at its level · small run
SWE-bench Pro (maker) 47.6% ▄●▂▂▄▂▆█·▂·▂ beats 2/16 hard · avg 30% 10% blunt · at its level · small run
coding · #117 of 469 · 71% of its score
Beats [Grok 4.3](grok-4-3.md), [Qwen3.5 Plus](qwen3-5-plus.md), [DeepSeek V3.2](deepseek-v3-2.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified (maker) 70.9% ▁▁▁▁▁▁▂▄●▆▁▁ beats 86/138 hard · avg 28% 33% sharp · at its level · small run
SWE-bench Multilingual (maker) 63.1% ▁··▁▃▁▅●█▂▂▁ beats 26/65 hard · avg 29% 29% at its level · small run
SWE-bench Pro (maker) 47.6% ▄●▂▂▄▂▆█·▂·▂ beats 2/16 hard · avg 30% 10% blunt · at its level · small run
source results share of the score
Poolside vendor-reported benchmark scor… 4 98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 64.4
## 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 2.1 (33B-A3B) (Q4_K_M, needs ~20.3 GB, from [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/1a37c0a5fb8c7a18e6106decb6be6327d1b63fa6))
```sh
llama cli -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf poolside/Laguna-XS-2.1-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 2.1 (33B-A3B) (Q4_K_M, needs ~20.3 GB, from [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/1a37c0a5fb8c7a18e6106decb6be6327d1b63fa6))
```sh
lms get https://huggingface.co/poolside/Laguna-XS-2.1-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-2.1-4bit -y` · 8bit (~35.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Laguna-XS-2.1-8bit -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 2.1 (33B-A3B) (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/laguna-xs-2.1/tags))
```sh
ollama run laguna-xs-2.1:q4_K_M
```
Other versions: Q8_0 (~36 GB) `ollama run laguna-xs-2.1:q8_0`
### 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 2.1 (33B-A3B) (4bit, needs ~18.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Laguna-XS-2.1-4bit/tree/c42e0a8f8d504ceacde015a535dcb286d65c8799))
```sh
mlx_lm.chat --model mlx-community/Laguna-XS-2.1-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Laguna-XS-2.1-4bit
```
Other versions: 8bit (~35.5 GB, Mac only) `mlx_lm.chat --model mlx-community/Laguna-XS-2.1-8bit`
### 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-2.1/blob/c5f36269bbdbd3f27fddc9a9f9dbae0cf2cf57db/README.md))
```sh
pip install 'vllm>=0.21.0' && vllm serve --model poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --served-model-name laguna --default-chat-template-kwargs '{"enable_thinking": true}'
```
Model repository and documentation: [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-XS-2.1)
[interactive version](https://whatmodel.sh/model/laguna-xs-2-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)