- MMLU-Pro 83.5
- GPQA-Diamond 82.2
- Humanity's Last Exam 18.3
- AIME 2026 92.3
- HMMT Feb 2026 78.4
- IMO Answer (IMO-AnswerBench) 78.6
- SciCode 37.4
- SWE Bench Verified 68.2
- Terminal-Bench 2.1 43.8
- τ3-Banking 14.2
- Claw-Eval 77.7
- IFEval 92.4
- IFBench 72.6
- OpenAI-MRCR (bins up to 128K, macro-average) 94.4
- AA-LCR 56.2
- Ko-LongBench (in-house) 89.6
- KMMLU-Pro 69.1
- Click 84.2
- HRM8K-KSM 91.1
- MMMLU (nine non-English supported languages) 86.6
- GlobalMMLU-Lite (nine non-English supported languages) 86.6
- PolyMath (nine non-English supported languages) 71.3
- KGC-Safety (in-house, Safe Rate) 99.8
- ROK-Fortress 89.5
K-EXAONE 2.0
K-EXAONE 2.0 ranks first on 2 of the 20 benchmarks with five or more models scored.
K-EXAONE 2.0
Open weights Flagship
- IFBench 72.6% #31 /80
- SciCode 40.1% #29 /49
- AA-LCR (Artificial Analysis Long Context Reasoning) 56.2% #28 /38
Where it ranks
Its best score on each benchmark, and where that ranks among every model with a score there. We rank each model once, at its best setting.
Knowledge and language
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| OpenAI MRCR | 94.4% | #1 of 10 | Lab model card | |
| IFEval | 92.4% | #18 of 135 | Lab model card | |
| OpenAI MRCR v2 | 94.4% | #2 of 14 | Lab model card | |
| MMLU-Pro | 83.5% | #48 of 167 | Lab model card | |
| MMMLU | 86.6% | #22 of 71 | Lab model card | |
| Ko-LongBench (LG) | 89.6% | #1 of 3 | Lab model card | |
| IFBench | 72.6% | #31 of 80 | Lab model card | |
| KMMLU-Pro | 69.1% | #3 of 7 | Lab model card | |
| CLIcK | 84.2% | #2 of 4 | Lab model card | |
| Humanity's Last Exam | 18.3% | #110 of 154 | Lab model card | |
| AA-LCR (Artificial Analysis Long Context Reasoning) | 56.2% | #28 of 38 | Lab model card | |
| Global MMLU Lite 3.0 | 86.6% | #4 of 4 | Lab model card |
Math and science
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| PolyMath | 71.3% | #5 of 24 | Lab model card | |
| GPQA Diamond | 82.2% | #104 of 302 | Lab model card | |
| HRM8K-KSM | 91.1% | #1 of 2 | Lab model card | |
| AIME 2026 | 92.3% | #24 of 38 | Lab model card | |
| IMO-AnswerBench | 78.6% | #28 of 37 | Lab model card | |
| HMMT 2026 | 78.4% | #18 of 19 | Lab model card |
Coding
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| SWE-bench Verified | 68.2% | #90 of 155 | Lab model card | |
| SciCode | 40.1% | #29 of 49 | Lab model card |
Agents and tools
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| Claw-Eval | 80% | #1 of 13 | Lab model card | |
| GAIA | 23.3% | #52 of 64 | Official leaderboard | |
| τ³-bench Banking | 14.2% | #19 of 22 | Lab model card | |
| Terminal-Bench 2.1 | 43.8% | #52 of 56 | Lab model card |
Safety and security
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| KGC-Safety (LG) | 99.8% | #1 of 2 | Lab model card | |
| ROK-FORTRESS | 89.5% | #1 of 1 | Lab model card |
Its lab's numbers put it at 145.0, number 131 of 404
The Self-Reported Index fits one score to the 18 capability results LG AI Research reported for this model, on the scale of the Epoch Capabilities Index. Labs choose which benchmarks to show and how to run them, so read it as the lab's claim, not an independent measurement. The 90% interval runs from 141.4 to 148.6. How we compute it.
- OpenAI MRCR v2 94.4
- OpenAI MRCR 94.4
- IFEval 92.4
- AIME 2026 92.3
- MMMLU 86.6
- MMLU-Pro 83.5
- GPQA Diamond 82.2
- Claw-Eval 80
- IMO-AnswerBench 78.6
- HMMT 2026 78.4
- IFBench 72.6
- PolyMath 71.3
- SWE-bench Verified 68.2
- AA-LCR (Artificial Analysis Long Context Reasoning) 56.2
- Terminal-Bench 2.1 43.8
- SciCode 40.1
- Humanity's Last Exam 18.3
- τ³-bench Banking 14.2
Its launch post
The benchmarks the lab chose to headline, with the scores it gave.
Its system card
Every result in the longer documents the lab published with it. 2 of them aren't in the launch post.
Capabilities
- MMLU-Pro 83.5
- GPQA Diamond 82.2
- Humanity's Last Exam 18.3
- AIME 2026 92.3
- HMMT 2026 78.4
- IMO-AnswerBench 78.6
- SciCode 40.1
- SWE-bench Verified 68.2
- Terminal-Bench 2.1 43.8
- τ³-bench Banking 14.2
- Claw-Eval 80
- IFEval 92.4
- IFBench 72.6
- OpenAI MRCR 94.4
- AA-LCR (Artificial Analysis Long Context Reasoning) 56.2
- Ko-LongBench (LG) 89.6
- KMMLU-Pro 69.1
- CLIcK 84.2
- HRM8K-KSM 91.1
- MMMLU 86.6
- Global MMLU Lite 3.0 86.6
- PolyMath 71.3
- WMT24++
Safety
- KGC-Safety (LG) 99.8
- ROK-FORTRESS 89.5