[whatmodel.sh](../../README.md) / DeepSeek LLM 67B Chat
# DeepSeek LLM 67B Chat
DeepSeek · released 2023-11 · 67B params · deepseek
**score 42.8** ±1.2 · #519 of 732 · from 11 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 DeepSeek LLM 67B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #255 of 469 10% 4
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #329 of 402 72% 3
math ▁▁●▅█▆▄█▄▂▁▁ #395 of 431* 11% 2
coding · #255 of 469 · 10% 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.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 47.0% ▃··▁▂▂▃▂●▃▄▂ beats 23/47 medium · avg 51% 3.8% sharp · at its level · noisy
CodeReviewQA: automated code refi… 39.7% ▁▁▁▁▁▁▂▂▃█●▄ beats 39/47 hard · avg 32% 3.1% typical
CodeReviewQA: change localisation… 37.8% █▂▁▁▂▂▂▃▅●▅▇ beats 31/47 hard · avg 33% 3.0% sharp · noisy
CodeReviewQA: change type recogni… 79.1% ▁▁▁······▁▃● beats 45/47 easy · avg 67% 0.1% blunt · at its level · noisy
chat & writing · #329 of 402 · 72% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 17.8% ▁▂▂▃▆●█▆▄▄▁▁ beats 47/122 medium · avg 35% 42% sharp
LMArena (text) 1185.0 ▁▁▁▂●▃▃▆▆▇█▃ beats 63/385 avg 1260 15% blunt
Polyrating rating (bias-aware re-… 1092.0 ▂▁▂▁▃▇●▆▇▄▂▂ beats 42/95 avg 1187 14% typical
math · #395 of 431 · 11% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 6.4% ▂●▅▆▄█▆▃▅▂▃▄ beats 4/92 hard · avg 28% 10% sharp · too hard for it
OTIS mock AIME 2024–2025 0.8% ●▃▄▃▃▃▇▇█▃▂▅ beats 7/197 very hard · avg 7.2% 0.7% sharp · too hard for it · noisy
other tests (2)
test result vs the others in its run difficulty counts why
GPQA Diamond 24.6% ●▁▁▁▂▄▆▄▅▇█▅ beats 2/211 hard · avg 39% 6.9% sharp · too hard for it
Chess Puzzles (Epoch) 0.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 15/137 very hard · avg 5.8% 0.2% sharp · too hard for it · noisy
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 42% ▇▇▇▇▇
Epoch AI 4 18% ▇▇
LMArena leaderboard dataset 1 15% ▇▇
Polyrating: A Cost-Effective and Bias-A… 1 14% ▇▇
CodeReviewQA: The Code Review Comprehen… 4 10% ▇
prior: pull toward the middle 1% ▇
weighted average = 42.8
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language 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 LLM 67B Chat (Q4_K_M, needs ~40.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/tree/bab8a570ac9271b502108dc7f80eae3881df430a))
```sh
llama cli -hf TheBloke/deepseek-llm-67b-chat-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-llm-67b-chat-GGUF:Q4_K_M
```
Other versions: Q2_K (~28.5 GB) `llama cli -hf TheBloke/deepseek-llm-67b-chat-GGUF:Q2_K`
### 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 LLM 67B Chat (Q4_K_M, needs ~40.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/tree/bab8a570ac9271b502108dc7f80eae3881df430a))
```sh
lms get https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q2_K (~28.5 GB) `lms get https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF@Q2_K -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 LLM 67B Chat (Q4_0, needs ~40.7 GB, from [ollama.com](https://ollama.com/library/deepseek-llm/tags))
```sh
ollama run deepseek-llm:67b-chat
```
Other versions: Q4_K_M (~40.7 GB) `ollama run deepseek-llm:67b-chat-q4_K_M`
Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
[interactive version](https://whatmodel.sh/model/deepseek-llm-67b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)