[whatmodel.sh](../../README.md) / K-EXAONE 2.0 (750B-A37B) # K-EXAONE 2.0 (750B-A37B) LG AI Research · released 2026-07 · 750B params (37B active) · apache-2.0 **score 66.2** ±1.3 · #123 of 732 · from 11 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks K-EXAONE 2.0 (750B-A37B) among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
agent tasks     ▁▁▃▆█▄▅●▄▄▂▁    #109 of 313     22%      3
coding          ▁▁▁▃▅▇█▇●▄▃▁    #99 of 469*     12%      1
chat & writing  ▁▁▁▃▆▄▆█▆●▇▃   #110 of 402*    5.2%      1
math            ▁▁▃▅█▆▄●▄▂▁▁    #115 of 431     38%      3

agent tasks · #109 of 313 · 22% of its score

Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [o3-pro](o3-pro.md), [Grok 4.3](grok-4-3.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Nova 2 Pro (Preview)](nova-2-pro.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall.

test                        result  vs the others in its run   difficulty            counts  why
SWE-bench Verified (maker)   68.2%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 74/138  hard · avg 19%           12%  sharp · at its level · small run
Terminal-Bench 2.1 (maker)   43.8%  ▁·▁·▁▁▄●▆█▅█ beats 17/63   very hard · avg 11%     8.1%  sharp · at its level · small run
τ³-bench Banking (maker)     14.2%  ▂▂·▅█▂▃●▃▆▂▅ beats 14/24   very hard · avg 6.1%    2.5%  blunt · small run

coding · #99 of 469 · 12% of its score

Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [Grok 4.3](grok-4-3.md), [Muse Glimmer 30B](muse-glimmer-30b.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md), [o4-mini](o4-mini.md) here, though they rank lower overall.

test                        result  vs the others in its run   difficulty      counts  why
SWE-bench Verified (maker)   68.2%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 74/138  hard · avg 19%     12%  sharp · at its level · small run

chat & writing · #110 of 402 · 5.2% of its score

Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [Mistral Large 3 (675B-A41B)](mistral-large-3.md), [Gemini 2.5 Flash](gemini-2-5-flash.md), [Gemma 4 12B](gemma-4-12b.md) here, though they rank lower overall.

test                                result  vs the others in its run    difficulty      counts  why
IFEval (aggregation unspecified) …   92.4%  ▁▂▄▂▂▄▆██●▂▁ beats 126/145  easy · avg 80%    5.2%  typical

math · #115 of 431 · 38% of its score

Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [gpt-oss-120b](gpt-oss-120b.md), [Qwen3-VL 32B Thinking](qwen3-vl-32b-thinking.md) here, though they rank lower overall.

test                        result  vs the others in its run  difficulty        counts  why
AIME 2026 (maker)            92.3%  ▁▁▁▂▂▂▆●▆▂·▁ beats 31/52  medium · avg 61%     15%  sharp · maxed out · small run
HMMT February 2026 (maker)   78.4%  ▃▃▂▅·██●█▅▃▅ beats 18/35  hard · avg 35%       14%  sharp · small run
IMO-AnswerBench (maker)      78.6%  ▁▁·▁▁▁▄●█▄▂▃ beats 20/44  medium · avg 38%    8.2%  small run

other tests (4)

test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond (maker)                 82.2%  ▁▁▁▁▂▄▅▅█●█▄ beats 216/322  hard · avg 43%           14%  sharp · small run
MMLU-Pro (maker)                     83.5%  ▁▁·▁▁▁▃▂▄▅●▁ beats 157/188  medium · avg 55%        8.5%  small run
Humanity's Last Exam (maker)         18.3%  ▁▁▁▄▆▆●██▆▁▃ beats 48/111   very hard · avg 4.2%    7.2%  small run
IFBench (aggregation unspecified)…   72.6%  ▂▆▂▄▄▄▂▄▄●█▇ beats 60/86    medium · avg 41%        4.2%  at its level · small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
LGAI-EXAONE/K-EXAONE-2.0-750B-A37B mode…       11   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 66.2

_{Licences: LGAI-EXAONE/K-EXAONE-2.0-750B-A37B model card ([Apache-2.0](https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B/blob/fec0c8dd25fdd20ffd09869d5af5277966d5523d/LICENSE))} ## How to run it ### Other ways to run it 1. Run it as published (fp8, needs ~752.09 GB, NVIDIA only, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-FP8/tree/b219707595c1330a95964abf9a06e38dae2f062c)) ```sh vllm serve LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-FP8 --served-model-name K-EXAONE-2.0-750B-A37B-FP8 --trust-remote-code --tensor-parallel-size 8 --kv-cache-dtype fp8_e4m3 --max-num-seqs 256 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml ``` Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B) [interactive version](https://whatmodel.sh/model/k-exaone-2-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)