[whatmodel.sh](../../README.md) / Laguna M.1 # Laguna M.1 Poolside · released 2026-04 · 225B params (23B active) · apache-2.0 **score 65.9** ±1.7 · #126 of 732 · from 4 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Laguna M.1 among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  █···█······●       #1 of 3     43%      4
coding       ▁▁▁▃▅▇█▇●▄▃▁   #123 of 469     85%      4
agent tasks · #1 of 3 · 43% of its score 
test                            result  vs the others in its run    difficulty           counts  why
SWE-bench Verified (maker)       74.6%  ▁▁▁▁▁▁▂▄█●▁▁ beats 103/138  hard · avg 28%          17%  sharp · at its level · small run
Terminal-Bench 2.0 (maker)       45.8%  ▂▂▅▅█●██▆▂▃▃ beats 16/36    very hard · avg 12%     14%  at its level · small run
SWE-bench Multilingual (maker)   63.1%  ▁··▁▃▁▅●█▂▂▁ beats 27/65    hard · avg 27%          11%  at its level · small run
SWE-bench Pro (maker)            49.2%  ▄▂●▂▄▂▆█·▂·▂ beats 3/16                               —  not counted yet: no other model in the …
coding · #123 of 469 · 85% 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), [Solar Pro 4](solar-pro-4.md), [Laguna XS 2.1 (33B-A3B)](laguna-xs-2-1.md) here, though they rank lower overall. 
test                            result  vs the others in its run    difficulty      counts  why
Arena WebDev (Code Arena)       1349.2  ▁▁▃▂●█▃▆▅▅▁▂ beats 27/129   avg 981            56%  sharp
SWE-bench Verified (maker)       74.6%  ▁▁▁▁▁▁▂▄█●▁▁ beats 103/138  hard · avg 28%     17%  sharp · at its level · small run
SWE-bench Multilingual (maker)   63.1%  ▁··▁▃▁▅●█▂▂▁ beats 27/65    hard · avg 27%     11%  at its level · small run
SWE-bench Pro (maker)            49.2%  ▄▂●▂▄▂▆█·▂·▂ beats 3/16                          —  not counted yet: no other model in the …
## 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   56% ▇▇▇▇▇▇▇
Poolside (submitted / maker-reported)        3   43% ▇▇▇▇▇
prior: pull toward the middle                     1% ▇
weighted average                                = 65.9
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-M.1 m…  SWE-bench Pro  49.2%  too few models have it yet
## How to run it ### Through an API api (Poolside) · price unknown · [huggingface.co](https://huggingface.co/poolside/Laguna-M.1/blob/2bf8a4ab6d2e8e1e0d92a41318532a0fee1640d6/README.md#pool) [context source](https://huggingface.co/poolside/Laguna-M.1/blob/2bf8a4ab6d2e8e1e0d92a41318532a0fee1640d6/README.md): Poolside official model card (poolside/Laguna-M.1, pinned): 'Context window: 262,144 tokens'. Observed 2026-09-25. ### 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 M.1 (Q4_K_M, needs ~136.1 GB, from [huggingface.co/linuxid10t](https://huggingface.co/linuxid10t/Laguna-M.1-GGUF/tree/3b5ee482c6894f80e696058271ea86d80ffcb848)) ```sh llama cli -hf linuxid10t/Laguna-M.1-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf linuxid10t/Laguna-M.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 M.1 (Q4_K_M, needs ~136.1 GB, from [huggingface.co/linuxid10t](https://huggingface.co/linuxid10t/Laguna-M.1-GGUF/tree/3b5ee482c6894f80e696058271ea86d80ffcb848)) ```sh lms get https://huggingface.co/linuxid10t/Laguna-M.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 (~127 GB, Mac only) `lms get https://huggingface.co/mlx-community/Laguna-M.1-4bit -y` · 8bit (~239.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Laguna-M.1-8bit -y` ### 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 M.1 (4bit, needs ~127 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Laguna-M.1-4bit/tree/5a397b458851eef77062f07c298752a5accfb8c3)) ```sh mlx_lm.chat --model mlx-community/Laguna-M.1-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Laguna-M.1-4bit ``` Other versions: 8bit (~239.9 GB, Mac only) `mlx_lm.chat --model mlx-community/Laguna-M.1-8bit` ### Other ways to run it 1. Run it as published (BF16, needs ~451.6 GB, NVIDIA only, from [huggingface.co/poolside](https://huggingface.co/poolside/Laguna-M.1/blob/2bf8a4ab6d2e8e1e0d92a41318532a0fee1640d6/README.md)) ```sh pip install 'vllm>=0.21.0' && vllm serve --model poolside/Laguna-M.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}' ``` Other versions: fp8 (~229.08 GB, NVIDIA only) `pip install 'vllm>=0.21.0' && vllm serve --model poolside/Laguna-M.1-FP8 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --served-model-name laguna --default-chat-template-kwargs '{"enable_thinking": true}'` · nvfp4 (~139.29 GB, NVIDIA only) `pip install 'vllm>=0.21.0' && vllm serve --model poolside/Laguna-M.1-NVFP4 --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-M.1) [interactive version](https://whatmodel.sh/model/laguna-m-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)