[whatmodel.sh](../../README.md) / DeepSeek R1 Distill Qwen 14B
# DeepSeek R1 Distill Qwen 14B
DeepSeek · released 2025-01 · 14.8B params · mit
**score 55.6** ±1.0 · #268 of 732 · from 18 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 DeepSeek R1 Distill Qwen 14B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #246 of 469 19% 5
math ▁▁▃▅█▆●█▄▂▁▁ #178 of 431 38% 5
coding · #246 of 469 · 19% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.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 (version unspecifie… 53.1% ▄····▄▄··●█▄ beats 3/6 medium · avg 42% 8.6% at its level
CodeReviewQA: change localisation… 53.2% █▂▁▁▂▂▂▃▅▅●▇ beats 37/47 hard · avg 33% 4.4% sharp · at its level · noisy
CodeReviewQA: automated code refi… 31.3% ▁▁▁▁▁▁▂▂▃●▅▄ beats 25/47 hard · avg 32% 3.2% at its level
CodeReviewQA: solution identifica… 66.8% ▃··▁▂▂▃▂█●▄▂ beats 36/47 medium · avg 51% 3.0% sharp · at its level · noisy
CodeReviewQA: change type recogni… 72.7% ▁▁▁······▁▃● beats 23/47 easy · avg 67% 0.1% blunt · at its level · noisy
math · #178 of 431 · 38% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md), [GPT-4.1](gpt-4-1.md) here, though they rank higher overall. Trails [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md), [OpenThinker2 32B](openthinker2-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 87.1% ▂▂▅▆▄█▆▃●▂▃▄ beats 73/92 hard · avg 28% 25% sharp · at its level
OTIS mock AIME 2024–2025 50.6% ▃▃▄▃▃▃●▇█▃▂▅ beats 76/197 very hard · avg 7.2% 6.3% sharp · noisy
AIME 2024 (maker) 69.7% ▃·▃·▃▃···●·▃ beats 4/7 medium · avg 50% 2.9% sharp · at its level · noisy
MATH-500 (maker) 93.9% ▄·▄▄·▄●█···▄ beats 4/7 very easy · avg 91% 2.4% maxed out
THINK-Bench final-answer accuracy… 70.2% ▄●·▄····▄▄·█ beats 2/7 easy · avg 76% 1.9% typical
other tests (7)
test result vs the others in its run difficulty counts why
GPQA Diamond 44.7% ▁▁▁▁▂▄●▄▅▇█▅ beats 49/211 hard · avg 39% 26% sharp · at its level · several runs
BRIDGE clinical text, zero-shot a… 34.3% ▁▁▁▁▁▂▂▃▄●█▃ beats 43/97 hard · avg 31% 6.8% blunt · at its level
SurgiQ surgical MCQ (zero-shot, l… 55.9% ▂···▂▂·▃▂●██ beats 7/17 medium · avg 53% 2.7% at its level
Open PT-LLM Leaderboard average (… 68.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 147/276 medium · avg 72% 2.3% at its level · noisy
EuroEval HellaSwag (English), tes… 72.6% ▃·▁▁▂▁▂▅●▇▄▁ beats 57/100 medium · avg 67% 1.8% sharp · at its level · noisy
Chess Puzzles (Epoch) 1.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 26/137 very hard · avg 5.8% 1.0% sharp · too hard for it · noisy
EuroEval Life in the UK (English … 53.8% ▁▁▁▁▁▁▁●▄█▄▃ beats 23/146 easy · avg 82% 1.0% noisy
source results share of the score
Epoch AI 4 49% ▇▇▇▇▇▇
deepseek-ai/DeepSeek-R1 model card (sub… 4 23% ▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 11% ▇
BRIDGE Medical Leaderboard 1 7% ▇
EuroEval leaderboards 2 3% ▇
SurgiQ: A Large-Scale Multi-Domain Benc… 1 3% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
THINK-Bench: Evaluating Thinking Effici… 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.6
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · deepseek-ai/DeepSeek-R1 model card ([mit](../../licenses/deepseek-ai--DeepSeek-R1.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 DeepSeek R1 Distill Qwen 14B (Q4_K_M, needs ~9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-GGUF/tree/45db3b6758edf0cf590ff9335c384492d2ee0b5c))
```sh
llama cli -hf lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-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/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~15.7 GB) `llama cli -hf lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-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 DeepSeek R1 Distill Qwen 14B (Q4_K_M, needs ~9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-GGUF/tree/45db3b6758edf0cf590ff9335c384492d2ee0b5c))
```sh
lms get https://huggingface.co/lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-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 (~15.7 GB) `lms get https://huggingface.co/lmstudio-community/DeepSeek-R1-Distill-Qwen-14B-GGUF@Q8_0 -y` · 4bit (~8.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit -y` · 8bit (~15.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Qwen-14B-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 DeepSeek R1 Distill Qwen 14B (Q4_K_M, needs ~9.38 GB, from [ollama.com](https://ollama.com/library/deepseek-r1/tags))
```sh
ollama run deepseek-r1:14b
```
Other versions: Q8_0 (~18.26 GB) `ollama run deepseek-r1:14b-qwen-distill-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 DeepSeek R1 Distill Qwen 14B (4bit, needs ~8.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit/tree/b39611728ed604538a2a18212ce33b02f99d8de2))
```sh
mlx_lm.chat --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit
```
Other versions: 8bit (~15.7 GB, Mac only) `mlx_lm.chat --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-8bit`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Apple M2 Pro, 32 GB q4_k_m 18.07 tok/s ollama (version not recorded) · l…
Apple M4, 16 GB q4_k_m 10.99 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
## Quantization evidence
How much of the full-precision result a quantized version kept, measured within one study. Rows from the same study share a table and aren't independent. They don't describe every download of that precision.
precision test full → quantized kept
FP8 W8A8 dynamic (FP8 weights… AIME 2024 pass@1 0.667 → 0.681 102.2%
FP8 W8A8 dynamic (FP8 weights… IFEval 0-shot instruction-level s… 0.43 → 0.437 101.5%
FP8 W8A8 dynamic (FP8 weights… GPQA Diamond pass@1 0.594 → 0.601 101.3%
FP8 W8A8 dynamic (FP8 weights… Reasoning average of 3 tasks (AIM… 0.736 → 0.743 101.0%
FP8 W8A8 dynamic (FP8 weights… OpenLLM v2 average of 6 tasks (IF… 0.342 → 0.344 100.5%
FP8 W8A8 dynamic (FP8 weights… MMLU-Pro 5-shot (OpenLLM v2 confi… 0.349 → 0.35 100.5%
FP8 W8A8 dynamic (FP8 weights… GSM8K 5-shot strict-match (lm-eva… 0.87 → 0.874 100.4%
FP8 W8A8 dynamic (FP8 weights… MMLU 5-shot acc (lm-evaluation-ha… 0.745 → 0.746 100.2%
FP8 W8A8 dynamic (FP8 weights… MATH-500 pass@1 0.947 → 0.946 100.0%
FP8 W8A8 dynamic (FP8 weights… OpenLLM v1 average of 6 tasks (AR… 0.71 → 0.708 99.7%
INT4 W4A16 (weights only, sym… MMLU-Pro 5-shot (OpenLLM v2 confi… 0.349 → 0.351 100.7%
INT4 W4A16 (weights only, sym… GSM8K 5-shot strict-match (lm-eva… 0.87 → 0.873 100.3%
INT4 W4A16 (weights only, sym… MATH-500 pass@1 0.947 → 0.95 100.3%
INT4 W4A16 (weights only, sym… OpenLLM v1 average of 6 tasks (AR… 0.71 → 0.709 99.8%
INT4 W4A16 (weights only, sym… AIME 2024 pass@1 0.667 → 0.66 99.1%
INT4 W4A16 (weights only, sym… Reasoning average of 3 tasks (AIM… 0.736 → 0.728 99.0%
INT4 W4A16 (weights only, sym… MMLU 5-shot acc (lm-evaluation-ha… 0.745 → 0.733 98.5%
INT4 W4A16 (weights only, sym… GPQA Diamond pass@1 0.594 → 0.575 96.8%
INT4 W4A16 (weights only, sym… OpenLLM v2 average of 6 tasks (IF… 0.342 → 0.324 94.7%
INT4 W4A16 (weights only, sym… IFEval 0-shot instruction-level s… 0.43 → 0.349 81.1%
INT8 W8A8 (weights symmetric … GSM8K 5-shot strict-match (lm-eva… 0.87 → 0.878 100.9%
INT8 W8A8 (weights symmetric … OpenLLM v1 average of 6 tasks (AR… 0.71 → 0.711 100.1%
INT8 W8A8 (weights symmetric … MATH-500 pass@1 0.947 → 0.947 100.0%
INT8 W8A8 (weights symmetric … MMLU 5-shot acc (lm-evaluation-ha… 0.745 → 0.743 99.7%
INT8 W8A8 (weights symmetric … AIME 2024 pass@1 0.667 → 0.663 99.5%
INT8 W8A8 (weights symmetric … Reasoning average of 3 tasks (AIM… 0.736 → 0.731 99.4%
INT8 W8A8 (weights symmetric … GPQA Diamond pass@1 0.594 → 0.583 98.3%
Each kept value links to its source.
[interactive version](https://whatmodel.sh/model/deepseek-r1-distill-qwen-14b) · [JSON](https://whatmodel.sh/api?format=json&all=1)