[whatmodel.sh](../../README.md) / s1.1 32B
# s1.1 32B
Simple Scaling · released 2025-02 · 32.76B params · apache-2.0
**score 53.0** ±1.1 · #317 of 732 · from 10 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 s1.1 32B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #215 of 469* 3.5% 1
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #249 of 402* 13% 2
math ▁▁▃▅█▆●█▄▂▁▁ #190 of 431 51% 3
coding · #215 of 469 · 3.5% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v2 65.5% ▄··█·▄···●▄█ beats 4/7 medium · avg 59% 3.5% at its level · small run
chat & writing · #249 of 402 · 13% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard v2.0 hard prompts (sty… 22.3% ▂·▃▅●·▃█▆▂▂▂ beats 8/22 very hard · avg 12% 10% sharp
Arena-Hard v2.0 creative writing … 8.2% ●▂▂▆▂▂▃█▂▃·▂ beats 0/22 hard · avg 19% 2.6% sharp · at its level · noisy
math · #190 of 431 · 51% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2024 59.3% ▁▁▁▂▁▃▁▅●██▃ beats 31/85 medium · avg 56% 23% sharp · at its level · several runs
MATH-500 87.4% ▁▃▂▃▄▇●▇█▄▂▁ beats 36/85 very easy · avg 86% 14% several runs
AIME 2025 42.7% ▁·▁▁▁▂●▆█▇▅▁ beats 28/133 medium · avg 37% 14% sharp · at its level
other tests (1)
test result vs the others in its run difficulty counts why
GPQA Diamond 62.0% ▁▁▁▁▂▄▅●███▄ beats 120/322 hard · avg 51% 33% sharp · at its level · several runs
source results share of the score
open-thoughts/OpenThinker2-32B model ca… 4 53% ▇▇▇▇▇▇
open-thoughts/OpenThinker-32B model card 4 34% ▇▇▇▇
Arena-Hard-Auto v2.0-Preview leaderboard 2 13% ▇▇
prior: pull toward the middle 1% ▇
weighted average = 53.0
Licences: open-thoughts/OpenThinker2-32B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · open-thoughts/OpenThinker-32B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v2.0-Preview leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-Apache-2.0.txt))
## 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 s1.1 32B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/simplescaling_s1.1-32B-GGUF/tree/8861817075c1ba50087374ecab401e4cce2a1839))
```sh
llama cli -hf bartowski/simplescaling_s1.1-32B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/simplescaling_s1.1-32B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.8 GB) `llama cli -hf bartowski/simplescaling_s1.1-32B-GGUF:Q8_0`
### 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 s1.1 32B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/simplescaling_s1.1-32B-GGUF/tree/8861817075c1ba50087374ecab401e4cce2a1839))
```sh
lms get https://huggingface.co/bartowski/simplescaling_s1.1-32B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~34.8 GB) `lms get https://huggingface.co/bartowski/simplescaling_s1.1-32B-GGUF@Q8_0 -y` · 4bit (~19.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/s1.1-32B-4bit -y` · 8bit (~34.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/simplescaling-s1.1-32B-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 s1.1 32B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/simplescaling_s1.1-32B-GGUF/tree/8861817075c1ba50087374ecab401e4cce2a1839))
```sh
ollama run hf.co/bartowski/simplescaling_s1.1-32B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.8 GB) `ollama run hf.co/bartowski/simplescaling_s1.1-32B-GGUF: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 s1.1 32B (4bit, needs ~19.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/s1.1-32B-4bit/tree/51e2d196b6322a20c07a75a26df52d9353323330))
```sh
mlx_lm.chat --model mlx-community/s1.1-32B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/s1.1-32B-4bit
```
Other versions: 8bit (~34.8 GB, Mac only) `mlx_lm.chat --model mlx-community/simplescaling-s1.1-32B-8bit`
### Other ways to run it
1. Run it as published (fp32, needs ~131.06 GB, NVIDIA only, from [huggingface.co/simplescaling](https://huggingface.co/simplescaling/s1.1-32B/tree/7b641db5bccde475f38ab3ee8345b4521ab12d64))
```sh
vllm serve simplescaling/s1.1-32B
```
Model repository and documentation: [huggingface.co/simplescaling](https://huggingface.co/simplescaling/s1.1-32B)
[interactive version](https://whatmodel.sh/model/s1-1-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)