LG-AI-EXAONE/EXAONE-4.0

LG-AI-EXAONE/EXAONE-4.0

Description: Official repository for EXAONE 4.0 built by LG AI Research

License: NOASSERTION

Stars: 105

Forks: 9

Open issues: 6

Created: 2025-07-07T06:45:57Z

Pushed: 2025-08-04T02:19:34Z

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README:

EXAONE-4.0

| 🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗 [What's Different?](#license) | |:---:|

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 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.

For more details, please refer to our technical report.

News

• 2025.07.26 : 🌟 EXAONE 4.0 is officially supported by HuggingFace transformers! Please check out the v4.54.0 release.
• 2025.07.18 : EXAONE 4.0 is officially supported by llama.cpp! Please check out the released version here.
• 2025.07.15 : We release EXAONE 4.0, a hybrid reasoning model with enhanced usability including 32B and 1.2B. Please check out these models!

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 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…