[whatmodel.sh](../../README.md) / Qwen2.5 Coder 14B Instruct
# Qwen2.5 Coder 14B Instruct
Alibaba · released 2024-11 · 14.77B params · apache-2.0
**score 55.8** ±1.2 · #264 of 732 · from 15 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2.5 Coder 14B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #216 of 469 83% 12
coding · #216 of 469 · 83% 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), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider code editing (133 Exercism … 69.2% ▁·▁▂▄▁▆▄█●▂▂ beats 46/62 medium · avg 51% 24% sharp · at its level
HumanEval (maker) 89.6% ▄·▄···█··●·▄ beats 5/6 easy · avg 84% 11% sharp
HumanEval+ (maker) 87.2% ▄·▄····▄▄·▄● beats 6/6 easy · avg 80% 10% sharp
MBPP (maker) 86.2% █···███·█●·█ beats 5/6 easy · avg 77% 7.3% typical
MultiPL-E (maker) 79.6% ▄·▄····▄▄·▄● beats 6/6 easy · avg 70% 6.5% typical
MBPP+ (maker) 72.8% ▄····▄▄·▄·●▄ beats 5/6 easy · avg 66% 6.1% at its level
CodeReviewQA: change localisation… 37.3% █▂▁▁▂▂▂▃▅●▅▇ beats 30/47 hard · avg 33% 5.4% sharp · at its level · noisy
CodeReviewQA: automated code refi… 46.6% ▁▁▁▁▁▁▂▂▃█▅● beats 45/47 hard · avg 32% 4.0% at its level
CodeReviewQA: solution identifica… 56.2% ▃··▁▂▂▃▂●▃▄▂ beats 31/47 medium · avg 51% 3.7% sharp · at its level · noisy
CRUXEval-I (maker) 69.5% █·██·█··█·●█ beats 5/6 medium · avg 56% 3.3% at its level
CRUXEval-O (maker) 79.5% ▄·█··▄·▄··●▄ beats 5/6 medium · avg 54% 1.2% blunt · at its level
CodeReviewQA: change type recogni… 73.9% ▁▁▁······▁▃● beats 27/47 easy · avg 67% 0.1% blunt · at its level · noisy
other tests (3)
test result vs the others in its run difficulty counts why
DSEffi-Bench Pass (1,000 data-sci… 32.8% ▅··▃●·█·▃██▃ beats 3/15 hard · avg 23% 8.9% at its level
BigCodeBench (Instruct) (maker) 48.4% ▄······▄▄█·● beats 5/6 medium · avg 37% 4.8% at its level
Open PT-LLM Leaderboard average (… 73.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 204/276 medium · avg 72% 2.9% at its level · noisy
source results share of the score
Qwen (submitted / maker-reported) 8 50% ▇▇▇▇▇▇
Aider code editing benchmark runs 1 24% ▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 13% ▇▇
DSEffi-Bench: Demystifying Large Langua… 1 9% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 55.8
Licences: Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation ([CC BY 4.0](../../licenses/paper-2608.30248v1-CC-BY-4.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-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 Qwen2.5 Coder 14B Instruct (Q4_K_M, needs ~9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-GGUF/tree/aa6da833ce5258d28435474d241249d7917498d6))
```sh
llama cli -hf lmstudio-community/Qwen2.5-Coder-14B-Instruct-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/Qwen2.5-Coder-14B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~15.7 GB) `llama cli -hf lmstudio-community/Qwen2.5-Coder-14B-Instruct-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 Qwen2.5 Coder 14B Instruct (Q4_K_M, needs ~9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-GGUF/tree/aa6da833ce5258d28435474d241249d7917498d6))
```sh
lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-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/Qwen2.5-Coder-14B-Instruct-GGUF@Q8_0 -y` · 4bit (~8.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-4bit -y` · 8bit (~15.7 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-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 Qwen2.5 Coder 14B Instruct (Q4_K_M, needs ~9.36 GB, from [ollama.com](https://ollama.com/library/qwen2.5-coder/tags))
```sh
ollama run qwen2.5-coder:14b-instruct-q4_K_M
```
Other versions: Q8_0 (~18.22 GB) `ollama run qwen2.5-coder:14b-instruct-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 Qwen2.5 Coder 14B Instruct (4bit, needs ~8.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-4bit/tree/4275f1d1fd379c8a5e8cc655c5a57ef03b912a29))
```sh
mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-4bit
```
Other versions: 8bit (~15.7 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-14B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
## 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 18.01 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/qwen2-5-coder-14b) · [JSON](https://whatmodel.sh/api?format=json&all=1)