medbench.kr

Qwen3.6 35B-A3B on clinicalqa@0

Run d982ddb4d0cc · baseline tier · evaluated 2026-10-29

Scores

accuracy0.903 (0.885–0.922)
parse rate0.998
n1045
tokens (in / out)379343 / 2224605
cost (USD)2.2815

By category

Allergy and Immunology0.917 (n=36)
Cardiology0.955 (n=89)
Dermatology0.917 (n=12)
Emergency Medicine0.969 (n=32)
Endocrinology0.926 (n=68)
Endocrinology, metabolic, and nutritional disorders1.000 (n=1)
Forensic Medicine1.000 (n=1)
Gastroenterology0.902 (n=112)
General Surgery0.948 (n=58)
Geriatric Medicine1.000 (n=1)
Gynecology0.814 (n=70)
Hematology and Oncology0.979 (n=48)
Infectious Diseases0.827 (n=52)
Musculoskeletal (e.g., costochondritis)1.000 (n=1)
Nephrology0.838 (n=111)
Neurology / Neurosurgery0.939 (n=49)
Obstetrics0.833 (n=18)
Ophthalmology1.000 (n=1)
Orthopedic Surgery0.958 (n=24)
Otolaryngology1.000 (n=14)
Otolaryngology (ENT)1.000 (n=14)
Pediatrics0.839 (n=56)
Pharmacology1.000 (n=1)
Psychiatry0.889 (n=45)
Pulmonology (Respiratory Medicine)0.923 (n=65)
Radiology1.000 (n=1)
Rheumatology0.898 (n=49)
Urology1.000 (n=13)
Vascular Surgery1.000 (n=2)
nan0.000 (n=1)

Reuse this result

Download per-item predictions (parquet) · JSON

@misc{medbench_d982ddb4d0cc,
  title = {Qwen3.6 35B-A3B on Korean clinical knowledge (KMLE-level MCQ) (clinicalqa@0)},
  author = {{medbench.kr}},
  year = {2026},
  howpublished = {\url{https://medbench.kr/r/d982ddb4d0cc}},
  note = {Evaluated 2026-10-29, harness p0.1, run key 7a3c09c15044}
}

Versions

model digestkgpu/qwen3.6-35b-a3b
task spec hash77e37cd0ca6bded7fba03792ec1922342b3f6a7dc3ce602881ce196e6aa7377c
harnessp0.1
run key7a3c09c150443edb289b4843e5fe019b9dc70e5dd982820587dea1f460e8a2a5
config{"max_tokens": 16384, "prompt": "clinicalqa-p0", "temperature": 0}

Data

ClinicalQA: Korean Medical Knowledge QA Benchmark, CC BY 4.0 (upstream Apache-2.0).

SNUH-HARI. ClinicalQA: Korean Medical Knowledge QA Benchmark. Seoul National University Hospital Healthcare AI Research Institute, 2025. doi:10.23421/2026.0800