[whatmodel.sh](../../README.md) / QwQ-32B-Preview
# QwQ-32B-Preview
Alibaba · released 2024-11 · 32.8B params · apache-2.0
**score 46.3** ±1.7 · #448 of 732 · from 8 benchmark results across 8 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks QwQ-32B-Preview among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ●▁▃▆█▄▅▅▄▄▂▁ #311 of 313* 6.7% 1
coding ▁▁▁▃▅▇█●▅▄▃▁ #157 of 469* 2.9% 1
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #353 of 402* 30% 1
agent tasks · #311 of 313 · 6.7% of its score
Trails [Granite 4.0 350M](granite-4-0-350m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen2.5 0.5B Instruct](qwen2-5-0-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 20.7% ▁●▁▁▁▃▅█▅▄▁▁ beats 2/114 medium · avg 51% 6.7% blunt · at its level
coding · #157 of 469 · 2.9% of its score
Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
LiveCodeBench official release_v5… 55.8% █···●····█·█ beats 1/3 medium · avg 51% 2.9% at its level · noisy
chat & writing · #353 of 402 · 30% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1154.2 ▁▁▁▂●▃▃▆▆▇█▃ beats 43/385 avg 1260 30% blunt
other tests (5)
test result vs the others in its run difficulty counts why
EvalPerf correctness (118 tasks, … 83.7% ▂·▂▂▂▆▅▆▆●▃█ beats 22/29 easy · avg 74% 20% at its level
BRIDGE clinical text, zero-shot a… 31.7% ▁▁▁▁▁▂▂▃●▆█▃ beats 35/97 hard · avg 31% 16% blunt · at its level
Open Persian LLM Leaderboard v2 a… 61.9% ▂▄▂▃█▄▃●▃▅▂▂ beats 56/81 medium · avg 57% 14% blunt · at its level
Open PT-LLM Leaderboard average (… 76.9% ▃▁▁▁▁▁▁▁▂█●▁ beats 238/276 medium · avg 72% 7.2% at its level · noisy
Vectara hallucination leaderboard… 83.9% ▁▁●▃▄▄█▆▃▂▂▁ beats 11/140 very easy · avg 94% 2.3% blunt · maxed out · noisy
source results share of the score
LMArena leaderboard dataset 1 30% ▇▇▇▇
EvalPlus / EvalPerf published evaluatio… 1 20% ▇▇
BRIDGE Medical Leaderboard 1 16% ▇▇
Open Persian LLM Leaderboard v2 1 14% ▇▇
Open Portuguese LLM Leaderboard 1 7% ▇
Berkeley Function Calling Leaderboard r… 1 7% ▇
LiveCodeBench official website results 1 3% ▇
Vectara hallucination leaderboard 1 2% ▇
prior: pull toward the middle 1% ▇
weighted average = 46.3
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · Vectara hallucination leaderboard ([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 QwQ-32B-Preview (Q4_K_M, needs ~19.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/QwQ-32B-Preview-GGUF/tree/34ef0b226cc2f7904107daa4b28e21c6c9ba01b7))
```sh
llama cli -hf lmstudio-community/QwQ-32B-Preview-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/QwQ-32B-Preview-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.8 GB) `llama cli -hf lmstudio-community/QwQ-32B-Preview-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 QwQ-32B-Preview (Q4_K_M, needs ~19.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/QwQ-32B-Preview-GGUF/tree/34ef0b226cc2f7904107daa4b28e21c6c9ba01b7))
```sh
lms get https://huggingface.co/lmstudio-community/QwQ-32B-Preview-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/QwQ-32B-Preview-GGUF@Q8_0 -y` · 4bit (~18.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen_QwQ-32B-Preview_MLX-4bit -y` · 8bit (~34.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/QwQ-32B-Preview-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 QwQ-32B-Preview (Q4_K_M, needs ~20.18 GB, from [ollama.com](https://ollama.com/library/qwq/tags))
```sh
ollama run qwq:32b-preview-q4_K_M
```
Other versions: Q8_0 (~39.86 GB) `ollama run qwq:32b-preview-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 QwQ-32B-Preview (4bit, needs ~18.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen_QwQ-32B-Preview_MLX-4bit/tree/8853fac2a4164670ded0f82000b718c050dbd7ab))
```sh
mlx_lm.chat --model mlx-community/Qwen_QwQ-32B-Preview_MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen_QwQ-32B-Preview_MLX-4bit
```
Other versions: 8bit (~34.8 GB, Mac only) `mlx_lm.chat --model mlx-community/QwQ-32B-Preview-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/QwQ-32B-Preview)
[interactive version](https://whatmodel.sh/model/qwq-32b-preview) · [JSON](https://whatmodel.sh/api?format=json&all=1)