LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-DSpark

K-EXAONE-2.0-750B-A37B-DSpark

Introduction

We introduce K-EXAONE 2.0, a frontier-scale multilingual language model developed by LG AI Research. K-EXAONE 2.0 was scaled to more than three times the size of its predecessor through upcycling, followed by continual pretraining, difficulty-focused mid-training, and post-training. K-EXAONE 2.0 is broadly competitive with leading open-weight models, demonstrating substantial improvements over its predecessor and achieving particularly strong results in long-context retrieval and safety.

Highlights

• Frontier-Class Scale

To build a large-scale foundation model with frontier-level intelligence, we upcycled the K-EXAONE model by expanding both its depth and width, resulting in a more favorable scaling curve. During this process, we found that clamping after two SwiGLU branches effectively mitigates the exploding activations in deeper layers, improving both training and inference stability.

• Advanced Reasoning & Agentic Intelligence

In response to the growth of agentic AI, we focused on expanding the model's capabilities in reasoning, agentic workflows, and long-context management. Through careful calibration of the training data and recipes, K-EXAONE 2.0 achieves consistent improvements in agentic coding and long-context understanding, with strong performance on long-context retrieval and safety.

• Production-Ready Inference

We support two speculative decoding methods to accelerate inference: MTP (Multi-Token Prediction) and DSpark. Both methods can speed up model generation by approximately 3–5×, reducing latency for long-horizon workloads such as agentic tasks.

• Multilinguality & Openness

We expanded multilingual coverage from six to ten languages: Korean, English, Spanish, German, Japanese, Vietnamese, French, Italian, Polish, and Portuguese. We also release K-EXAONE 2.0 under the Apache license 2.0 so that the broader AI ecosystem can inspect, deploy, and build upon it.


Model Configurations

Number of Parameters 750B

Active Parameters 37B

Hidden Dimension 6,144

Intermediate Size 18,432

Number of Layers 78 (2 heading Dense + 76 Sparse) Main layers

5 DSpark layers

Attention 1 x Global (NoPE)

1 x 4096 SWA

19 x [3 x 128 SWA + 1 x Global] Blocks

Attention Heads 64 Q-heads / 8 KV-heads

Head Dimension 128

Number of Experts 1 Shared Expert

256 Total Experts

8 Activated Experts

Expert Dimension 2,048

Vocab Size 153,600

Context Length 262,144

Knowledge Cutoff 2025 2Q

Evaluation Results

The following table shows the benchmark results for the K-EXAONE 2.0 BF16 model. Detailed evaluation results and configurations can be found in our technical report.

K-EXAONE 2.0 K-EXAONE Qwen3.5 GLM-5.1 DSV4 Pro (max)

Architecture MoE MoE MoE MoE MoE

Total Params 750B 236B 397B 754B 1.6T

Active Params 37B 23B 17B 40B 49B

World Knowledge

MMLU-Pro 83.5 83.8 89.8 86.0 87.5

GPQA-Diamond 82.2 79.1 88.4 86.2 90.1

Humanity's Last Exam 18.3 13.6 28.7 31.0 37.7

Math

AIME 2026 92.3 92.2 91.3 95.3 95.2

HMMT Feb 2026 78.4 80.7 84.6 82.6 95.2

IMO Answer 78.6 76.3 80.9 83.8 89.8

Coding / Agentic Coding

SciCode 37.4 35.6 42.0 43.8 50.0

SWE Bench Verified 68.2 49.4 76.4 73.6 80.6

Terminal-Bench 2.1 43.8 30.3 51.3 61.8 64.0

Agentic Tool Use

τ3-Banking 14.2 14.2 13.4 11.5 25.8

Claw-Eval 77.7 70.3 79.7 84.4 82.7

Instruction Following

IFEval 92.4 89.7 92.6 93.9 94.0

IFBench 72.6 67.3 76.5 76.3 76.5

Long Context Understanding

OpenAI-MRCR 94.4 52.3 93.0 71.5 92.9

AA-LCR 56.2 53.5 65.7 62.3 66.3

Ko-LongBench 89.6 86.8 91.3 83.6 91.4

Korean

KMMLU-Pro 69.1 67.3 77.4 75.8 80.5

Click 84.2 83.9 88.9 88.7 91.6

HRM8K-KSM 91.1 91.9 91.2 89.4 94.3

Multilinguality

MMMLU 86.6 86.2 90.6 <td