AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

LLM Stats

Aider-Polyglot Edit

A challenging multi-language coding benchmark that evaluates models' code editing abilities across C++, Go, Java, JavaScript, Python, and Rust. Contains 225 of Exercism's most difficult programming problems, selected as problems that were solved by 3 or fewer out of 7 top coding models. The benchmark focuses on code editing tasks and measures both correctness of solutions and proper edit format usage. Designed to re-calibrate evaluation scales so top models score between 5-50%.

评测信息与证据

来源
LLM Stats
发布者
未收录
评测发布日期
未收录
采集日期
2026-08-17
模态
text

本站保留来源报告的结果,不代表独立复测。请先核对任务版本、评测协议、分数单位和报告日期;不同运行条件不能直接合并为统一排名。

原始资料

报告成绩 10

按来源中的观察顺序展示,顺序不代表排名。日期保留原始精度。

模型数值单位协议报告日期与口径来源
DeepSeek-V30.797未收录未收录2024-12-25 · model_announcementLLM Stats
Gemini 2.5 Pro0.727未收录未收录2025-05-20 · model_announcementLLM Stats
o3-mini0.604未收录未收录2025-01-30 · model_announcementLLM Stats
o4-mini0.582未收录未收录2025-04-16 · model_announcementLLM Stats
Gemini 2.5 Flash0.567未收录未收录2025-05-20 · model_announcementLLM Stats
GPT-4.10.529未收录未收录2025-04-14 · model_announcementLLM Stats
GPT-4.50.449未收录未收录2025-02-27 · model_announcementLLM Stats
GPT-4.1 mini0.316未收录未收录2025-04-14 · model_announcementLLM Stats
GPT-4o0.182未收录未收录2024-08-06 · model_announcementLLM Stats
GPT-4.1 nano0.062未收录未收录2025-04-14 · model_announcementLLM Stats