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EXAONE-4.0-32B

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exaone4
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exaone-4.0
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EXAONE-4.0-32B

Introduction

We introduce EXAONE 4.0, which integrates a Non-reasoning mode and Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean.

The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimized for high performance, and a small-size 1.2B model designed for on-device applications.

In the EXAONE 4.0 architecture, we apply new architectural changes compared to previous EXAONE models as below:

  1. Hybrid Attention: For the 32B model, we adopt hybrid attention scheme, which combines Local attention (sliding window attention) with Global attention (full attention) in a 3:1 ratio. We do not use RoPE (Rotary Positional Embedding) for global attention for better global context understanding.
  2. QK-Reorder-Norm: We adopt the Post-LN (LayerNorm) scheme for transformer blocks instead of Pre-LN, and we add RMS normalization right after the Q and K projection. It helps yield better performance on downstream tasks despite consuming more computation.

For more details, please refer to our technical report, blog, and GitHub.

Model Configuration

  • Number of Parameters (without embeddings): 30.95B
  • Number of Layers: 64
  • Number of Attention Heads: GQA with 40-heads and 8-KV heads
  • Vocab Size: 102,400
  • Context Length: 131,072 tokens

Quickstart

You should install the transformers library forked from the original, available in our PR. Once this PR is merged and released, we will update this section.

You can install the latest version of transformers with support for EXAONE 4.0 by following the command:

pip install git+https://github.com/lgai-exaone/transformers@add-exaone4

Non-reasoning mode

For general use, you can use the EXAONE 4.0 models with the following example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "LGAI-EXAONE/EXAONE-4.0-32B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# choose your prompt
prompt = "Explain how wonderful you are"
prompt = "Explica lo increíble que eres"
prompt = "너가 얼마나 대단한지 설명해 봐"

messages = [
    {"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

output = model.generate(
    input_ids.to(model.device),
    max_new_tokens=128,
    do_sample=False,
)
print(tokenizer.decode(output[0]))

Reasoning mode

The EXAONE 4.0 models have reasoning capabilities for handling complex problems. You can activate reasoning mode by using the enable_thinking=True argument with the tokenizer, which opens a reasoning block that starts with <think> tag without closing it.

messages = [
    {"role": "user", "content": "Which one is bigger, 3.12 vs 3.9?"}
]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    enable_thinking=True,
)

output = model.generate(
    input_ids.to(model.device),
    max_new_tokens=128,
    do_sample=True,
    temperature=0.6,
    top_p=0.95
)
print(tokenizer.decode(output[0]))

The model generation with reasoning mode can be affected sensitively by sampling parameters, so please refer to the Usage Guideline for better quality.

Agentic tool use

The EXAONE 4.0 models can be used as agents with their tool calling capabilities. You can provide tool schemas to the model for effective tool calling.

import random

def roll_dice(max_num: int):
    return random.randint(1, max_num)

tools = [
    {
        "type": "function",
        "function": {
            "name": "roll_dice",
            "description": "Roll a dice with the number 1 to N. User can select the number N.",
            "parameters": {
                "type": "object",
                "required": ["max_num"],
                "properties": {
                    "max_num": {
                        "type": "int",
                        "description": "Max number of the dice"
                    }
                }
            }
        }
    }
]

messages = [
    {"role": "user", "content": "Roll D6 dice twice!"}
]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    tools=tools,
)

output = model.generate(
    input_ids.to(model.device),
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
)
print(tokenizer.decode(output[0]))

Deployment

TensorRT-LLM

TensorRT-LLM officially supports EXAONE 4.0 models in the latest commits. Before it is released, you need to clone the TensorRT-LLM repository to build from source.

git clone https://github.com/NVIDIA/TensorRT-LLM.git

After cloning the repository, you need to build the source for installation. Please refer to the official documentation for a guide to build the TensorRT-LLM environment.

You can run the TensorRT-LLM server by following steps:

  1. Write extra configuration YAML file

    # extra_llm_api_config.yaml
kv_cache_config:
  enable_block_reuse: false

  2. Run server with the configuration

    trtllm-serve serve [MODEL_PATH] --backend pytorch --extra_llm_api_options extra_llm_api_config.yaml

For more details, please refer to the documentation of EXAONE from TensorRT-LLM.

Other inference engines including vllm and sglang don't support the EXAONE 4.0 officially now. We will update as soon as these libraries are updated.

Performance

The following tables show the evaluation results of each model, with reasoning and non-reasoning mode. The evaluation details can be found in the technical report.

  • ✅ denotes the model has a hybrid reasoning capability, evaluated by selecting reasoning / non-reasoning on the purpose.
  • To assess Korean practical and professional knowledge, we adopt both the KMMLU-Redux and KMMLU-Pro benchmarks. Both datasets are publicly released!

32B Reasoning Mode

EXAONE 4.0 32B Phi 4 reasoning-plus Magistral Small-2506 Qwen 3 32B Qwen 3 235B DeepSeek R1-0528
Model Size 32.0B 14.7B 23.6B 32.8B 235B 671B
Hybrid Reasoning ✅ ✅ ✅
World Knowledge
MMLU-Redux 92.3 90.8 86.8 90.9 92.7 93.4
MMLU-Pro 81.8 76.0 73.4 80.0 83.0 85.0
GPQA-Diamond 75.4 68.9 68.2 68.4 71.1 81.0
Math/Coding
AIME 2025 85.3 78.0 62.8 72.9 81.5 87.5
HMMT Feb 2025 72.9 53.6 43.5 50.4 62.5 79.4
LiveCodeBench v5 72.6 51.7 55.8 65.7 70.7 75.2
LiveCodeBench v6 66.7 47.1 47.4 60.1 58.9 70.3
Instruction Following
IFEval 83.7 84.9 37.9 85.0 83.4 80.8
Multi-IF (EN) 73.5 56.1 27.4 73.4 73.4 72.0
Agentic Tool Use
BFCL-v3 63.9 N/A 40.4 70.3 70.8 64.7
Tau-bench (Airline) 51.5 N/A 38.5 34.5 37.5 53.5
Tau-bench (Retail) 62.8 N/A 10.2 55.2 58.3 63.9
Multilinguality
KMMLU-Pro 67.7 55.8 51.5 61.4 68.1 71.7
KMMLU-Redux 72.7 62.7 54.6 67.5 74.5 77.0
KSM 87.6 79.8 71.9 82.8 86.2 86.7
MMMLU (ES) 85.6 84.3 68.9 82.8 86.7 88.2
MATH500 (ES) 95.8 94.2 83.5 94.3 95.1 96.0

32B Non-Reasoning Mode

EXAONE 4.0 32B Phi 4 Mistral-Small-2506 Gemma 3 27B Qwen3 32B Qwen3 235B Llama-4-Maverick DeepSeek V3-0324
Model Size 32.0B 14.7B 24.0B 27.4B 32.8B 235B 402B 671B
Hybrid Reasoning ✅ ✅ ✅
World Knowledge
MMLU-Redux 89.8 88.3 85.9 85.0 85.7 89.2 92.3 92.3
MMLU-Pro 77.6 70.4 69.1 67.5 74.4 77.4 80.5 81.2
GPQA-Diamond 63.7 56.1 46.1 42.4 54.6 62.9 69.8 68.4
Math/Coding
AIME 2025 35.9 17.8 30.2 23.8 20.2 24.7 18.0 50.0
HMMT Feb 2025 21.8 4.0 16.9 10.3 9.8 11.9 7.3 29.2
LiveCodeBench v5 43.3 24.6 25.8 27.5 31.3 35.3 43.4 46.7
LiveCodeBench v6 43.1 27.4 26.9 29.7 28.0 31.4 32.7 44.0
Instruction Following
IFEval 84.8 63.0 77.8 82.6 83.2 83.2 85.4 81.2
Multi-IF (EN) 71.6 47.7 63.2 72.1 71.9 72.5 77.9 68.3
Long Context
HELMET 58.3 N/A 61.9 58.3 54.5 63.3 13.7 N/A
RULER 88.2 N/A 71.8 66.0 85.6 90.6 2.9 N/A
LongBench v1 48.1 N/A 51.5 51.5 44.2 45.3 34.7 N/A
Agentic Tool Use
BFCL-v3 65.2 N/A 57.7 N/A 63.0 68.0 52.9 63.8
Tau-Bench (Airline) 25.5 N/A 36.1 N/A 16.0 27.0 38.0 40.5
Tau-Bench (Retail) 55.9 N/A 35.5 N/A 47.6 56.5 6.5 68.5
Multilinguality
KMMLU-Pro 60.0 44.8 51.0 50.7 58.3 64.4 68.8 67.3
KMMLU-Redux 64.8 50.1 53.6 53.3 64.4 71.7 76.9 72.2
KSM 59.8 29.1 35.5 36.1 41.3 46.6 40.6 63.5
Ko-LongBench 76.9 N/A 55.4 72.0 73.9 74.6 65.6 N/A
MMMLU (ES) 80.6 81.2 78.4 78.7 82.1 83.7 86.9 86.7
MATH500 (ES) 87.3 78.2 83.4 86.8 84.7 87.2 78.7 89.2
WMT24++ (ES) 90.7 89.3 92.2 93.1 91.4 92.9 92.7 94.3

1.2B Reasoning Mode

EXAONE 4.0 1.2B EXAONE Deep 2.4B Qwen 3 0.6B Qwen 3 1.7B SmolLM3 3B
Model Size 1.28B 2.41B 596M 1.72B 3.08B
Hybrid Reasoning ✅ ✅ ✅ ✅
World Knowledge
MMLU-Redux 71.5 68.9 55.6 73.9 74.8
MMLU-Pro 59.3 56.4 38.3 57.7 57.8
GPQA-Diamond 52.0 54.3 27.9 40.1 41.7
Math/Coding
AIME 2025 45.2 47.9 15.1 36.8 36.7
HMMT Feb 2025 34.0 27.3 7.0 21.8 26.0
LiveCodeBench v5 44.6 47.2 12.3 33.2 27.6
LiveCodeBench v6 45.3 43.1 16.4 29.9 29.1
Instruction Following
IFEval 67.8 71.0 59.2 72.5 71.2
Multi-IF (EN) 53.9 54.5 37.5 53.5 47.5
Agentic Tool Use
BFCL-v3 52.9 N/A 46.4 56.6 37.1
Tau-Bench (Airline) 20.5 N/A 22.0 31.0 37.0
Tau-Bench (Retail) 28.1 N/A 3.3 6.5 5.4
Multilinguality
KMMLU-Pro 42.7 24.6 21.6 38.3 30.5
KMMLU-Redux 46.9 25.0 24.5 38.0 33.7
KSM 60.6 60.9 22.8 52.9 49.7
MMMLU (ES) 62.4 51.4 48.8 64.5 64.7
MATH500 (ES) 88.8 84.5 70.6 87.9 87.5

1.2B Non-Reasoning Mode

EXAONE 4.0 1.2B Qwen 3 0.6B Gemma 3 1B Qwen 3 1.7B SmolLM3 3B
Model Size 1.28B 596M 1.00B 1.72B 3.08B
Hybrid Reasoning ✅ ✅ ✅ ✅
World Knowledge
MMLU-Redux 66.9 44.6 40.9 63.4 65.0
MMLU-Pro 52.0 26.6 14.7 43.7 43.6
GPQA-Diamond 40.1 22.9 19.2 28.6 35.7
Math/Coding
AIME 2025 23.5 2.6 2.1 9.8 9.3
HMMT Feb 2025 13.0 1.0 1.5 5.1 4.7
LiveCodeBench v5 26.4 3.6 1.8 11.6 11.4
LiveCodeBench v6 30.1 6.9 2.3 16.6 20.6
Instruction Following
IFEval 74.7 54.5 80.2 68.2 76.7
Multi-IF (EN) 62.1 37.5 32.5 51.0 51.9
Long Context
HELMET 41.2 21.1 N/A 33.8 38.6
RULER 77.4 55.1 N/A 65.9 66.3
LongBench v1 36.9 32.4 N/A 41.9 39.9
Agentic Tool Use
BFCL-v3 55.7 44.1 N/A 52.2 47.3
Tau-Bench (Airline) 10.0 31.5 N/A 13.5 38.0
Tau-Bench (Retail) 21.7 5.7 N/A 4.6 6.7
Multilinguality
KMMLU-Pro 37.5 24.6 9.7 29.5 27.6
KMMLU-Redux 40.4 22.8 19.4 29.8 26.4
KSM 26.3 0.1 22.8 16.3 16.1
Ko-LongBench 69.8 16.4 N/A 57.1 15.7
MMMLU (ES) 54.6 39.5 35.9 54.3 55.1
MATH500 (ES) 71.2 38.5 41.2 66.0 62.4
WMT24++ (ES) 65.9 58.2 76.9 76.7 84.0

Usage Guideline

To achieve the expected performance, we recommend using the following configurations:

  • For non-reasoning mode, we recommend using a lower temperature value such as temperature<0.6 for better performance.
  • For reasoning mode (using <think> block), we recommend using temperature=0.6 and top_p=0.95.
    • If you suffer from the model degeneration, we recommend using presence_penalty=1.5.
  • For Korean general conversation with 1.2B model, we suggest to use temperature=0.1 to avoid code switching.

Limitation

The EXAONE language model has certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we have made every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by EXAONE language model does not reflect the views of LG AI Research.

  • Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
  • Biased responses may be generated, which are associated with age, gender, race, and so on.
  • The generated responses rely heavily on statistics from the training data, which can result in the generation of semantically or syntactically incorrect sentences.
  • Since the model does not reflect the latest information, the responses may be false or contradictory.

LG AI Research strives to reduce potential risks that may arise from EXAONE language models. Users are not allowed to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate outputs violating LG AI's ethical principles when using EXAONE language models.

License

The model is licensed under EXAONE AI Model License Agreement 1.2 - NC

The main difference from the older version is as below:

  • We removed the claim of model output ownership from the license.
  • We restrict the model use against the development of models that compete with EXAONE.
  • We allow the model to be used for educational purposes, not just research.

Citation 

@article{exaone-4.0,
  title={EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes},
  author={{LG AI Research}},
  journal={arXiv preprint arXiv:2507.11407},
  year={2025}
}

Contact

LG AI Research Technical Support: [email protected]

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