general benchmark
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%.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | DE | 79.7% | 100.0% | 10 | C | |
| 02 | GO | 72.7% | 88.9% | 10 | C | |
| 03 | OP | 60.4% | 77.8% | 10 | C | |
| 04 | OP | 58.2% | 66.7% | 10 | C | |
| 05 | GO | 56.7% | 55.6% | 10 | C | |
| 06 | OP | 52.9% | 44.4% | 10 | C | |
| 07 | OP | 44.9% | 33.3% | 10 | C | |
| 08 | OP | 31.6% | 22.2% | 10 | C | |
| 09 | OP | 18.2% | 11.1% | 10 | C | |
| 10 | OP | 6.2% | 0.0% | 10 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What Aider-Polyglot Edit measures and how its scores work.
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%.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Aider-Polyglot Edit.
DeepSeek-V3 is currently ranked first with 79.7%.
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%.
Yes. Higher values rank better for this benchmark.
10 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.