[whatmodel.sh](../../README.md) / A.X K1 (519B-A33B) # A.X K1 (519B-A33B) SK Telecom · released 2025-12 · 518.98B params (33B active) · apache-2.0 **score 61.4** ±1.2 · #192 of 732 · from 7 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 A.X K1 (519B-A33B) among every model on that task or test, worst to best.}

task    vs the others          rank  counts  tests
coding  ▁▁▁▃▅▇█●▅▄▃▁   #161 of 469*     15%      2
math    ▁▁▃▅█▆▄●▄▂▁▁   #125 of 431*     10%      1

coding · #161 of 469 · 15% of its score

Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench v6 (Feb–May 2025) (…   74.9%  ▄··●····▄▄·█ beats 1/6    medium · avg 47%    8.4%  at its level
SciCode (level unspecified) (make…   24.1%  ●·····▄█▄▄·▄ beats 0/6    hard · avg 16%      6.2%  at its level

math · #125 of 431 · 10% 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), [Granite 4.2 8B](granite-4-2-8b.md), [Nemotron-Cascade-14B-Thinking](nemotron-cascade-14b-thinking.md) here, though they rank lower overall.

test               result  vs the others in its run  difficulty        counts  why
AIME 2026 (maker)   91.3%  ▄·▄·●▄··█··▄ beats 2/6    medium · avg 57%     10%  sharp · noisy

other tests (4)

test                                result  vs the others in its run  difficulty            counts  why
GPQA Diamond (maker)                 76.4%  ●·····▄█▄▄·▄ beats 0/6    hard · avg 44%           41%  sharp · at its level
Global-MMLU-Lite (maker)             77.8%  ●······▅··█▃ beats 0/6    medium · avg 61%         17%  at its level
IFBench (aggregation unspecified)…   63.2%  ●······█▃▃·▃ beats 0/6    medium · avg 45%        8.7%  at its level
Humanity's Last Exam (maker)          8.3%  ●········█·▂ beats 0/6    very hard · avg 5.4%    8.3%  typical

## 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
A.X K2 Technical Report (submitted / ma…        7   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 61.4

_{Licences: A.X K2 Technical Report ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/))} ## How to run it ### Other ways to run it 1. Run it as published (BF16, needs ~1038 GB, NVIDIA only, from [huggingface.co/skt](https://huggingface.co/skt/A.X-K1/tree/bb56f50f36b6c0b7b0c108da02db47a5e3bd9ee1)) ```sh vllm serve skt/A.X-K1 --tensor-parallel-size 8 ``` Model repository and documentation: [huggingface.co/skt](https://huggingface.co/skt/A.X-K1) [interactive version](https://whatmodel.sh/model/ax-k1) · [JSON](https://whatmodel.sh/api?format=json&all=1)