[whatmodel.sh](../../README.md) / DeepSeek-Coder 33B Instruct
# DeepSeek-Coder 33B Instruct
DeepSeek · released 2023-11 · 33.34B params · deepseek
**score 51.1** ±0.6 · #346 of 732 · from 16 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks DeepSeek-Coder 33B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #267 of 469 74% 10
coding · #267 of 469 · 74% 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 [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [Phi-3 Medium 128K Instruct](phi-3-medium-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 81.1% ▁·▁▁▃▄▆▅●▃▁▁ beats 85/99 easy · avg 76% 35% sharp · several runs
HumanEval+ 75.0% ▁·▁▁▁▃▄▄▄█●▁ beats 85/99 easy · avg 70% 20% sharp · at its level
LiveCodeBench official (Jan 1–Nov… 21.9% ▁▁·▁▂●▆█▅▂·▂ beats 7/31 hard · avg 23% 8.1% sharp · at its level
MBPP+ 70.1% ▁···▁▁▂▅▇█●▂ beats 65/75 easy · avg 66% 4.3% at its level
CRUXEval-I 46.5% ▁··▁▅▂█●▁··▁ beats 17/22 medium · avg 52% 1.5% at its level
CodeReviewQA: change localisation… 3.2% █▂▁●▂▂▂▃▅▅▅▇ beats 12/47 hard · avg 33% 1.5% sharp · at its level · noisy
CRUXEval-O 49.9% ▁·▂▃▁█▁●···▁ beats 21/22 medium · avg 51% 1.5% blunt · at its level
CodeReviewQA: solution identifica… 9.0% ▃··▁●▂▃▂█▃▄▂ beats 8/47 medium · avg 51% 1.3% sharp · at its level · noisy
CodeReviewQA: automated code refi… 32.8% ▁▁▁▁▁▁▂▂▃●▅▄ beats 28/47 hard · avg 32% 1.1% at its level
CodeReviewQA: change type recogni… 69.8% ▁▁▁······▁▃● beats 19/47 easy · avg 67% 0.0% blunt · at its level · noisy
other tests (5)
test result vs the others in its run difficulty counts why
EvoEval (overall) 56.0% ▂▂▃▄▄█▁▄▅▃●▃ beats 39/42 medium · avg 52% 12% at its level
BigCodeBench (Instruct) 41.8% ▃▂·▆▆██▆●▃▃▃ beats 27/33 medium · avg 39% 4.9% at its level
BigCodeBench (Complete) 51.1% ▂▁▁▄▂▅▇█▆●▂▂ beats 52/58 medium · avg 48% 4.7% at its level
EvalPerf correctness (118 tasks, … 77.5% ▂·▂▂▂▆▅●▆▆▃█ beats 13/29 easy · avg 74% 2.3% typical
DSEffi-Bench Pass (1,000 data-sci… 17.5% ●··▃▅·█·▃██▃ beats 1/15 hard · avg 23% 2.3% typical
source results share of the score
EvalPlus / EvalPerf published evaluatio… 4 48% ▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 25% ▇▇▇
BigCodeBench paper 2 10% ▇
LiveCodeBench official website results 1 8% ▇
CodeReviewQA: The Code Review Comprehen… 4 4% ▇
CRUXEval published evaluation results 2 3% ▇
DSEffi-Bench: Demystifying Large Langua… 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 51.1
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.txt)) · 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))
## 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-Coder 33B Instruct (Q4_K_M, needs ~19.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/tree/cd6a4d02d3502901ff7f980b6373387ab8b8e91a))
```sh
llama cli -hf TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.4 GB) `llama cli -hf TheBloke/deepseek-coder-33B-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 DeepSeek-Coder 33B Instruct (Q4_K_M, needs ~19.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/tree/cd6a4d02d3502901ff7f980b6373387ab8b8e91a))
```sh
lms get https://huggingface.co/TheBloke/deepseek-coder-33B-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 (~35.4 GB) `lms get https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF@Q8_0 -y` · 4bit (~18.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/deepseek-coder-33b-instruct -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-Coder 33B Instruct (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/deepseek-coder/tags))
```sh
ollama run deepseek-coder:33b-instruct-q4_K_M
```
Other versions: Q8_0 (~35 GB) `ollama run deepseek-coder:33b-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 DeepSeek-Coder 33B Instruct (4bit, needs ~18.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/deepseek-coder-33b-instruct/tree/3a7af2876fffb60723ef24c4312e9150ccded76d))
```sh
mlx_lm.chat --model mlx-community/deepseek-coder-33b-instruct
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/deepseek-coder-33b-instruct
```
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct)
[interactive version](https://whatmodel.sh/model/deepseek-coder-33b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)