SK Telecom released A.X 4.0 (pronounced "A dot X"), a large language model (LLM) optimized for Korean-language understanding and enterprise deployment, on July 03, 2025. Built on the open-source Qwen2.5 model, A.X 4.0 has been further trained with large-scale Korean datasets to deliver outstanding performance in real-world business environments.

Superior Korean Proficiency: Achieved a score of 78.3 on KMMLU, the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming GPT-4o (72.5).
Deep Cultural Understanding: Scored 83.5 on CLIcK, a benchmark for Korean cultural and contextual comprehension, surpassing GPT-4o (80.2).
Efficient Token Usage: A.X 4.0 uses approximately 33% fewer tokens than GPT-4o for the same Korean input, enabling more cost-effective and efficient processing.
Deployment Flexibility: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light).
Long Context Handling: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length)

Performance

Model Performance

Benchmarks	A.X 4.0	Qwen3-235B-A22B (w/o reasoning)	Qwen2.5-72B	GPT-4o
Knowledge	KMMLU	78.32	73.64	66.44	72.51
KMMLU-pro	72.43	64.4	56.27	66.97
KMMLU-redux	74.18	71.17	58.76	69.08
CLIcK	83.51	74.55	72.59	80.22
KoBALT	47.30	41.57	37.00	44.00
MMLU	86.62	87.37	85.70	88.70
General	Ko-MT-Bench	86.69	88.00	82.69	88.44
MT-Bench	83.25	86.56	93.50	88.19
LiveBench^2024.11	52.30	64.50	54.20	52.19
Instruction Following	Ko-IFEval	77.96	77.53	77.07	75.38
IFEval	86.05	85.77	86.54	83.86
Math	HRM8K	48.55	54.52	46.37	43.27
MATH	74.28	72.72	77.00	72.38
Code	HumanEval+	79.27	79.27	81.71	86.00
MBPP+	73.28	70.11	75.66	75.10
LiveCodeBench^{2024.10~2025.04}	26.07	33.09	27.58	29.30
Long Context	LongBench^<128K	56.70	49.40	45.60	47.50
Tool-use	FunctionChatBench	85.96	82.43	88.30	95.70

Lightweight Model Performance

Benchmarks	A.X 4.0 Light	Qwen3-8B (w/o reasoning)	Qwen2.5-7B	EXAONE-3.5-7.8B	Kanana-1.5-8B
Knowledge	KMMLU	64.15	63.53	49.56	53.76	48.28
KMMLU-pro	50.28	50.71	38.87	40.11	37.63
KMMLU-redux	56.05	55.74	38.58	42.21	35.33
CLIcK	68.05	62.71	60.56	64.30	61.30
KoBALT	30.29	26.57	21.57	21.71	23.14
MMLU	75.43	82.89	75.40	72.20	68.82
General	Ko-MT-Bench	79.50	64.06	61.31	81.06	76.30
MT-Bench	81.56	65.69	79.37	83.50	77.60
LiveBench	37.10	50.20	37.00	40.20	29.40
Instruction Following	Ko-IFEval	72.99	73.39	60.73	65.01	69.96
IFEval	84.68	85.38	76.73	82.61	80.11
Math	HRM8K	40.12	52.50	35.13	31.88	30.87
MATH	68.88	71.48	65.58	63.20	59.28
Code	HumanEval+	75.61	77.44	74.39	76.83	76.83
MBPP+	67.20	62.17	68.50	64.29	67.99
LiveCodeBench	18.03	23.93	16.62	17.98	16.52

🚀 Quickstart

with HuggingFace Transformers

transformers>=4.46.0 or the latest version is required to use skt/A.X-4.0

pip install transformers>=4.46.0

Example Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "skt/A.X-4.0"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "당신은 사용자가 제공하는 영어 문장들을 한국어로 번역하는 AI 전문가입니다."},
    {"role": "user", "content": "The first human went into space and orbited the Earth on April 12, 1961."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=128,
        do_sample=False,
    )

len_input_prompt = len(input_ids[0])
response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True)
print(response)
# Output:
# 최초의 인간이 1961년 4월 12일에 우주로 가서 지구 궤도를 돌았습니다.

with vLLM

vllm>=v0.6.4.post1 or the latest version is required to use tool-use function

bash

pip install vllm>=v0.6.4.post1
# if you don't want to activate tool-use function, just commenting out below vLLM option
VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes"
vllm serve skt/A.X-4.0 $VLLM_OPTION

Example Usage

python

from openai import OpenAI

def call(messages, model):
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
    )
    print(completion.choices[0].message)

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="api_key"
)
model = "skt/A.X-4.0"
messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='여름철 에어컨 적정 온도는 24~26도입니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)

messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Response in a single sentence."}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='A comfortable and energy-efficient temperature for air conditioning in summer is typically around 24-26°C (75-79°F).', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)

Examples for tool-use

python

from openai import OpenAI


def call(messages, model):
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
        tools=tools
    )
    print(completion.choices[0].message)


client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="api_key"
)
model = "skt/A.X-4.0"

calculate_discount = {
    "type": "function",
    "function": {
        "name": "calculate_discount",
        "description": "원가격과 할인율(퍼센트 단위)을 입력받아 할인된 가격을계산한다.",
        "parameters": {
            "type": "object",
            "properties": {
                "original_price": {
                    "type": "number",
                    "description": "상품의 원래 가격"
                },
                "discount_percentage": {
                    "type": "number",
                    "description": "적용할 할인율(예: 20% 할인의 경우 20을 입력)"
                }
            },
            "required": ["original_price", "discount_percentage"]
        }
    }
}
get_exchange_rate = {
    "type": "function",
    "function": {
        "name": "get_exchange_rate",
        "description": "두 통화 간의 환율을 가져온다.",
        "parameters": {
            "type": "object",
            "properties": {
                "base_currency": {
                    "type": "string",
                    "description": "The currency to convert from."
                },
                "target_currency": {
                    "type": "string",
                    "description": "The currency to convert to."
                }
            },
            "required": ["base_currency", "target_currency"]
        }
    }
}
tools = [calculate_discount, get_exchange_rate]

### Slot filling ###
messages = [{"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)


### Function calling ###
messages = [
    {"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"},
    {"role": "assistant", "content": "직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?"},
    {"role": "user", "content": "15% 할인 받을 수 있어."},
]
call(messages, model)
# Output: 
# ChatCompletionMessage(content=None, refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='chatcmpl-tool-8634f423e5494ebfa7a428b8385b4981', function=Function(arguments='{"original_price": 57600, "discount_percentage": 15}', name='calculate_discount'), type='function')], reasoning_content=None)


### Completion ###
messages = [
    {"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"},
    {"role": "assistant", "content": "직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?"},
    {"role": "user", "content": "15% 할인 받을 수 있어."},
    {"role": "tool", "tool_call_id": "random_id", "name": "calculate_discount", "content": "{\"original_price\": 57600, \"discount_percentage\": 15, \"discounted_price\": 48960.0}"}
]
call(messages, model)
# Output: 
# ChatCompletionMessage(content='15% 할인을 적용하면 상품의 가격은 48,960원이 됩니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)

Citation

@article{SKTAdotX4,
  title={A.X 4.0},
  author={SKT AI Model Lab},
  year={2025},
  url={https://huggingface.co/skt/A.X-4.0}
}

Contact

Business & Partnership Contact: a.x@sk.com

OpenLLM-Korea/A.X-4.0

A.X 4.0

A.X 4.0 Family Highlights

Performance

Model Performance

Lightweight Model Performance

🚀 Quickstart

with HuggingFace Transformers

Example Usage

with vLLM

Example Usage

Examples for tool-use

Citation

Contact

OpenLLM-Korea/
A.X-4.0