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%.

Evaluation context and evidence

Source
LLM Stats
Publisher
Not recorded
Benchmark release
Not recorded
Collected
2026-08-17
Modality
text

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Original sources

Reported results 10

Shown in source observation order, not ranked. Dates retain the precision provided by the source.

ModelValueUnitProtocolReported date and basisSource
DeepSeek-V30.797Not recordedNot recorded2024-12-25 · model_announcementLLM Stats
Gemini 2.5 Pro0.727Not recordedNot recorded2025-05-20 · model_announcementLLM Stats
o3-mini0.604Not recordedNot recorded2025-01-30 · model_announcementLLM Stats
o4-mini0.582Not recordedNot recorded2025-04-16 · model_announcementLLM Stats
Gemini 2.5 Flash0.567Not recordedNot recorded2025-05-20 · model_announcementLLM Stats
GPT-4.10.529Not recordedNot recorded2025-04-14 · model_announcementLLM Stats
GPT-4.50.449Not recordedNot recorded2025-02-27 · model_announcementLLM Stats
GPT-4.1 mini0.316Not recordedNot recorded2025-04-14 · model_announcementLLM Stats
GPT-4o0.182Not recordedNot recorded2024-08-06 · model_announcementLLM Stats
GPT-4.1 nano0.062Not recordedNot recorded2025-04-14 · model_announcementLLM Stats