whatmodel.sh / EXAONE 3.5 7.8B Instruct

EXAONE 3.5 7.8B Instruct

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EXAONE 3.5 7.8B Instruct is an open-weights language model from LG AI Research, released December 2024. Its published size is 7.82B parameters and a 33k-token context window. Licence: exaone.

Catalogue identity notes

LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct @553ea25 (created 2024-12-01, EXAONE AI Model License (non-commercial terms)). EXAONE 3.5 bilingual (English/Korean) instruct model, sibling of the catalogued EXAONE 3.5 2.4B. Lab rows only: rule-(b) rows pooled in first-party-lgai-scores (IFEval and Arena-Hard runs with the catalogued EXAONE models).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 7,818,448,896 parameters.

50.2±3.8#435 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of EXAONE 3.5 7.8B Instruct, out of 1132 with a score, then more from LG AI Research.

#modelscorewhereRAMreleased
564Jamba Large 1.7 AI21 Labs50±15api price unknown +local~103GB2025-07
565Llama-3-8B-ProLong-512k-Instruct Princeton NLP50±5local~35GB2024-08
566EXAONE 3.5 7.8B Instruct LG AI Research50±4local~6.1GB2024-12
567EXAONE Deep 2.4B LG AI Research50±2local~3.2GB2025-03
568FireFunction v2 (70B) Fireworks AI50±6local~24GB2024-06
more from LG AI Research
146K-EXAONE 2.0 (750B-A37B) LG AI Research67±1local~791GB2026-07
190K-EXAONE (236B-A23B) LG AI Research65±1local~152GB2025-12
192EXAONE 4.5 33B LG AI Research65±2local~24GB2026-04

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #242 of 466 * · 94% of its score · 2 tests

Beats Granite 4.2 8B, Mistral Small 4 (119B-A6.5B), Olmo 3.1 32B Think here, though they rank higher overall. Trails Flan-T5 XXL, LFM2.5 1.2B Instruct, Gemma 3 4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena-Hard (version/judge unspecified) lab68.7% ✎▃▃▂▅▃▆▅█▅▅▂▃ beats 18/30easy · avg 68%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%at its level · small run
IFEval (aggregation unspecified) lab78.9% ✎▄·▄·▄·▄▄···█ beats 2/6easy · avg 78%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 37%typical

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run EXAONE 3.5 7.8B Instruct · needs ~4.77 GB · from huggingface.co/LGAI-EXAONE
    chat in the terminal
    llama cli -hf LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run EXAONE 3.5 7.8B Instruct · needs ~4.77 GB · from huggingface.co/LGAI-EXAONE

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run EXAONE 3.5 7.8B Instruct · needs ~4.8 GB · from ollama.com
    chat in the terminal
    ollama run exaone3.5:7.8b

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run EXAONE 3.5 7.8B Instruct · needs ~4.4 GB, Mac only · from huggingface.co/alexander583
    chat in the terminal
    mlx_lm.chat --model alexander583/EXAONE-3.5-7.8B-Instruct-Q4-mlx
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model alexander583/EXAONE-3.5-7.8B-Instruct-Q4-mlx

Weights and documentation: huggingface.co/LGAI-EXAONE ↗

$ how the score was worked out · LG AI Research vendor-reporte… 94%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
LG AI Research vendor-reported benchmark scores for its own EXAONE models (Hugging Face model cards) · lab-reported2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%Factual vendor-reported score (not licensed content)
prior · pull toward the middle▇▇ 6%
weighted average= 50.2

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json