MedQA (USMLE) ↗90.22 %
openai/o1-mini-2024-09-12 · MedQA · Accuracy ↗
OpenAI / MODEL EVIDENCE
openai/o1-mini-2024-09-12
2 benchmarks · 4 observations · 2 sources
Scores at a glance
2 benchmarks · large dot: openai/o1-mini-2024-09-12; small dots: closest models where reported
UGPhysics ↗49.96%
OpenAI-o1-mini-2024-09-12 · UGPhysics · English · overall accuracy · original evaluation ↗
2 more reported results
49.6% · OpenAI-o1-mini-2024-09-12 · UGPhysics · Chinese · overall accuracy · original evaluation · UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models ↗
49.78% · OpenAI-o1-mini-2024-09-12 · UGPhysics · Language average · overall accuracy · original evaluation · UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models — published result table ↗
All 9 authors
Xin Xu, Qiyun Xu, Tong Xiao, Tianhao Chen, Yuchen Yan, Jiaxin Zhang, Shizhe Diao, Can Yang, Yang Wang
- MedQA — evaluation methodology and results ↗
Names used in source tables
OpenAI-o1-mini-2024-09-12 · openai/o1-mini-2024-09-12