[whatmodel.sh](../../README.md) / EXAONE 4.5 33B # EXAONE 4.5 33B LG AI Research · released 2026-04 · 34.4B params · exaone **score 63.5** ±1.6 · #163 of 732 · from 7 benchmark results across 2 sources · † 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 4.5 33B among every model on that task or test, worst to best. 
task                vs the others            rank  counts  tests
agent tasks         ▁▁▃▆█▄●▅▄▄▂▁     #123 of 313*     15%      1
chat & writing      ▁▁▁▃▆▄▆█●▇▇▃     #144 of 402*    4.6%      1
math                ▁▁▃▅█▆▄●▄▂▁▁      #81 of 431*     19%      1
vision & documents                 not ranked yet       —      3
agent tasks · #123 of 313 · 15% of its score Beats [o3-pro](o3-pro.md), [o3](o3.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank lower overall. 
test                      result  vs the others in its run  difficulty      counts  why
τ²-bench Telecom (maker)   73.0%  ▁·▅█▆▅●▃▃▂▃▁ beats 57/89  hard · avg 17%     15%  sharp · at its level · noisy · small run
chat & writing · #144 of 402 · 4.6% of its score Beats [o3-mini](o3-mini.md), [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md) here, though they rank higher overall. Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [Spark-X2.5 1.7B](spark-x2-5-1-7b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
IFEval (aggregation unspecified) …   89.6%  ▄·▄·▄·▄▄···● beats 5/6    easy · avg 78%    4.6%  typical
math · #81 of 431 · 19% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md), [Qwen3.5 397B-A17B](qwen3-5-397b-a17b.md) here, though they rank higher overall. Trails [Granite 4.2 30B](granite-4-2-30b.md), [Qwen3-Next 80B-A3B Instruct](qwen3-next-80b-a3b-instruct.md) here, though they rank lower overall. 
test               result  vs the others in its run  difficulty        counts  why
AIME 2025 (maker)   92.9%  █·▄▄····▄··● beats 6/6    medium · avg 54%     19%  sharp
vision & documents · 0.0% of its score 
test                                result  vs the others in its run   difficulty      counts  why
CharXiv (reasoning questions) (ma…   71.7%  ▂▅▃▅▅▆▆●▆█▆▂ beats 62/101  hard · avg 34%       —  not counted yet: no other model in the …
MMMU-Pro standard (SqueezeBits lm…   17.6%  no other model yet                              —  not counted yet: no other model in the …
MMMU-Pro vision (SqueezeBits lmms…   21.5%  no other model yet                              —  not counted yet: no other model in the …
other tests (4) 
test                                result  vs the others in its run    difficulty           counts  why
GPQA Diamond (maker)                 80.5%  █···▄▄···▄·● beats 6/6      hard · avg 51%          27%  sharp
KOPA-Bench (pass@1) (maker)          25.9%  ▄·█··●··█▄·█ beats 3/8      very hard · avg 14%     14%  at its level
MMLU-Pro (maker)                     83.3%  ▁▁·▁▁▁▃▂▄▅●▁ beats 155/188  medium · avg 62%        13%  typical
BFCL multi-turn (version not stat…   53.0%  ▄▄···▄·▄●▄▄▄ beats 5/8      hard · avg 26%         6.9%  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   78% ▇▇▇▇▇▇▇▇▇
Multi-Step Tool-Calling over Korean Ope…        2   21% ▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 63.5
Licences: Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
Not counted (4) 
source                  test                            score  why
SqueezeBits             MMLU-Pro                        72.0%  only model in this source's run
SqueezeBits             MMMU-Pro standard (SqueezeBit…  17.6%  too few models have it yet
SqueezeBits             MMMU-Pro vision (SqueezeBits …  21.5%  too few models have it yet
LG AI Research vendor…  CharXiv (reasoning questions)   71.7%  too few models have it yet
## 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 4.5 33B (gguf-q4_k_m, needs ~21.14 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/tree/main)) ```sh llama cli -hf LGAI-EXAONE/EXAONE-4.5-33B-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-4.5-33B-GGUF:Q4_K_M ``` Other versions: gguf-q8_0 (~41.78 GB) `llama cli -hf LGAI-EXAONE/EXAONE-4.5-33B-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 EXAONE 4.5 33B (gguf-q4_k_m, needs ~21.14 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/tree/main)) ```sh lms get https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: gguf-q8_0 (~41.78 GB) `lms get https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF@Q8_0 -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 EXAONE 4.5 33B (gguf-q4_k_m, needs ~21.14 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/tree/main)) ```sh ollama run hf.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M ``` Other versions: gguf-q8_0 (~41.78 GB) `ollama run hf.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q8_0` Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B) [interactive version](https://whatmodel.sh/model/exaone-4-5-33b) · [JSON](https://whatmodel.sh/api?format=json&all=1)