[whatmodel.sh](../../README.md) / EXAONE Deep 32B # EXAONE Deep 32B LG AI Research · released 2025-03 · 32B params · exaone **score 55.4** ±1.5 · #271 of 732 · from 5 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks EXAONE Deep 32B among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  ██·········●      #1 of 3*     10%      1
math    ▁▁▃▅█●▄█▄▂▁▁   #200 of 431     56%      3

coding · #1 of 3 · 10% of its score

test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench (version unspecifie…   59.5%  ▁···▁▁▁█●▃▄▁ beats 24/38  medium · avg 45%     10%  at its level · small run

math · #200 of 431 · 56% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank lower overall.

test               result  vs the others in its run  difficulty           counts  why
AIME 2025 (maker)   65.8%  █·▄●····▄··█ beats 3/6    medium · avg 54%        28%  sharp · at its level
AIME 2024 (maker)   72.1%  ▁▁▁▂▁▃▁▅●██▃ beats 43/85  medium · avg 56%        18%  sharp · at its level · small run
MATH-500 (maker)    95.7%  ▁▃▂▃▄▇▅●█▄▂▁ beats 57/85  very easy · avg 92%    9.3%  maxed out · small run

other tests (1)

test                  result  vs the others in its run  difficulty      counts  why
GPQA Diamond (maker)   66.1%  █···▄●···▄·█ beats 3/6    hard · avg 51%     34%  sharp · at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
LG AI Research vendor-reported benchmar…        5   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 55.4

## 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 EXAONE Deep 32B (Q4_K_M, needs ~19.34 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B-GGUF/tree/4ba3a5454685aa7fda2b77c62c810879c084d78c)) ```sh llama cli -hf LGAI-EXAONE/EXAONE-Deep-32B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf LGAI-EXAONE/EXAONE-Deep-32B-GGUF:Q4_K_M ``` ### 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 EXAONE Deep 32B (Q4_K_M, needs ~19.34 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B-GGUF/tree/4ba3a5454685aa7fda2b77c62c810879c084d78c)) ```sh lms get https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run EXAONE Deep 32B (Q4_K_M, needs ~19 GB, from [ollama.com](https://ollama.com/library/exaone-deep/tags)) ```sh ollama run exaone-deep:32b ``` Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B) [interactive version](https://whatmodel.sh/model/exaone-deep-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)