[whatmodel.sh](../../README.md) / OpenThinker 32B
# OpenThinker 32B
Open Thoughts · released 2025-02 · 32.76B params · apache-2.0
**score 54.5** ±1.2 · #296 of 732 · from 8 benchmark results across 2 sources · † 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 OpenThinker 32B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█●▅▄▃▁ #185 of 469* 4.0% 1
math ▁▁▃▅█▆●█▄▂▁▁ #184 of 431 57% 3
coding · #185 of 469 · 4.0% of its score
Beats [Qwen3.5 9B](qwen3-5-9b.md), [Gemini 2.5 Flash](gemini-2-5-flash.md), [Apriel 1.6 15B Thinker](apriel-1-6-15b-thinker.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v2 (maker) 68.9% ▄··█·▄···▄●█ beats 5/7 medium · avg 59% 4.0% at its level · small run
math · #184 of 431 · 57% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [GPT-4.1](gpt-4-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md) here, though they rank higher overall. Trails [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2024 (maker) 68.0% ▁▁▁▂▁▃▁▅●██▃ beats 34/85 medium · avg 56% 26% sharp · at its level · several runs
AIME 2025 (maker) 49.3% ▁·▁▁▁▂▅●█▇▅▁ beats 35/133 medium · avg 37% 16% sharp · at its level
MATH-500 (maker) 90.6% ▁▃▂▃▄▇●▇█▄▂▁ beats 41/85 very easy · avg 86% 15% several runs
other tests (1)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 63.5% ▁▁▁▁▂▄▅●███▄ beats 125/322 hard · avg 51% 38% sharp · at its level · several runs
source results share of the score
open-thoughts/OpenThinker2-32B model ca… 4 61% ▇▇▇▇▇▇▇
open-thoughts/OpenThinker-32B model car… 4 38% ▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 54.5
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))
## 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 OpenThinker 32B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/OpenThinker-32B-GGUF/tree/076af7c70d6236dbc79408db7c73128eb5fc915d))
```sh
llama cli -hf lmstudio-community/OpenThinker-32B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/OpenThinker-32B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.8 GB) `llama cli -hf lmstudio-community/OpenThinker-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 OpenThinker 32B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/OpenThinker-32B-GGUF/tree/076af7c70d6236dbc79408db7c73128eb5fc915d))
```sh
lms get https://huggingface.co/lmstudio-community/OpenThinker-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/lmstudio-community/OpenThinker-32B-GGUF@Q8_0 -y` · 4bit (~18.4 GB, Mac only) `lms get https://huggingface.co/AlejandroOlmedo/OpenThinker-32B-4bit-mlx -y` · 8bit (~34.8 GB, Mac only) `lms get https://huggingface.co/AlejandroOlmedo/OpenThinker-32B-8bit-mlx -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 OpenThinker 32B (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/openthinker/tags))
```sh
ollama run openthinker:32b
```
### 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 OpenThinker 32B (4bit, needs ~18.4 GB, Mac only, from [huggingface.co/AlejandroOlmedo](https://huggingface.co/AlejandroOlmedo/OpenThinker-32B-4bit-mlx/tree/bb34642083152e902eaf509e428c1954e8da6025))
```sh
mlx_lm.chat --model AlejandroOlmedo/OpenThinker-32B-4bit-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model AlejandroOlmedo/OpenThinker-32B-4bit-mlx
```
Other versions: 8bit (~34.8 GB, Mac only) `mlx_lm.chat --model AlejandroOlmedo/OpenThinker-32B-8bit-mlx`
Model repository and documentation: [huggingface.co/open-thoughts](https://huggingface.co/open-thoughts/OpenThinker-32B)
[interactive version](https://whatmodel.sh/model/openthinker-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)