medbench.kr

GPT-6 Luna on clinicalqa@0

Run d15ecb67085b · baseline tier · evaluated 2026-10-29

Scores

accuracy0.900 (0.882–0.919)
parse rate1.000
n1045
tokens (in / out)394098 / 141736
cost (USD)0.1103

By category

Allergy and Immunology0.944 (n=36)
Cardiology0.944 (n=89)
Dermatology0.917 (n=12)
Emergency Medicine0.969 (n=32)
Endocrinology0.882 (n=68)
Endocrinology, metabolic, and nutritional disorders1.000 (n=1)
Forensic Medicine1.000 (n=1)
Gastroenterology0.911 (n=112)
General Surgery0.931 (n=58)
Geriatric Medicine1.000 (n=1)
Gynecology0.814 (n=70)
Hematology and Oncology1.000 (n=48)
Infectious Diseases0.808 (n=52)
Musculoskeletal (e.g., costochondritis)1.000 (n=1)
Nephrology0.883 (n=111)
Neurology / Neurosurgery0.918 (n=49)
Obstetrics0.778 (n=18)
Ophthalmology1.000 (n=1)
Orthopedic Surgery0.917 (n=24)
Otolaryngology0.929 (n=14)
Otolaryngology (ENT)1.000 (n=14)
Pediatrics0.839 (n=56)
Pharmacology1.000 (n=1)
Psychiatry0.867 (n=45)
Pulmonology (Respiratory Medicine)0.954 (n=65)
Radiology1.000 (n=1)
Rheumatology0.918 (n=49)
Urology0.769 (n=13)
Vascular Surgery1.000 (n=2)
nan0.000 (n=1)

Reuse this result

Download per-item predictions (parquet) · JSON

@misc{medbench_d15ecb67085b,
  title = {GPT-6 Luna on Korean clinical knowledge (KMLE-level MCQ) (clinicalqa@0)},
  author = {{medbench.kr}},
  year = {2026},
  howpublished = {\url{https://medbench.kr/r/d15ecb67085b}},
  note = {Evaluated 2026-10-29, harness p0.1, run key c3113631e2d4}
}

Versions

model digestopenrouter/openai/gpt-6-luna
task spec hash77e37cd0ca6bded7fba03792ec1922342b3f6a7dc3ce602881ce196e6aa7377c
harnessp0.1
run keyc3113631e2d46944bc1717761a8c19b6604e3a2c0bef73ae6018c756830cea6f
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