🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗
✈️ Try on FriendliAI (licensed under commercial purposes)
📢 EXAONE 4.0 is officially supported by llama.cpp! Please check the guide below
EXAONE-4.0-1.2B-GGUF
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:
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.
QK-Reorder-Norm: We reorder the LayerNorm position from the traditional Pre-LN scheme by applying LayerNorm directly to the attention and MLP outputs, and we add RMS normalization right after the Q and K projection. It helps yield better performance on downstream tasks despite consuming more computation.
This process is necessary to avoid issues with current EXAONE modeling code in llama.cpp. This is work in progress at our PR. We will update this once these issues are solved.
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_name ="LGAI-EXAONE/EXAONE-4.0-1.2B"4tokenizer = AutoTokenizer.from_pretrained(model_name)56messages =[7{"role":"user","content":"Let's work together on local system!"}8]9input_text = tokenizer.apply_chat_template(10 messages,11 tokenize=False,12 add_generation_prompt=True,13)1415print(repr(input_text))16withopen("inputs.txt","w")as f:17 f.write(input_text)
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!
The evaluation results are based on the original model, not quantized model.
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
Gemma3 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
SmolLM 3 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
SmolLM 3 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
[!IMPORTANT]
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.
[!NOTE]
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}
}
