general benchmark
A multi-language code-editing benchmark containing 225 difficult Exercism programming problems across C++, Go, Java, JavaScript, Python, and Rust.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelDE | Score79.70% | Percentile100.00% | Participants10 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score72.70% | Percentile88.89% | Participants10 | EvidenceC | Evaluated |
| Rank03 | ModelOP | Score60.40% | Percentile77.78% | Participants10 | EvidenceC | Evaluated |
| Rank04 | ModelOP | Score58.20% | Percentile66.67% | Participants10 | EvidenceC | Evaluated |
| Rank05 | ModelGO | Score56.70% | Percentile55.56% | Participants10 | EvidenceC | Evaluated |
| Rank06 | ModelOP | Score52.90% | Percentile44.44% | Participants10 | EvidenceC | Evaluated |
| Rank07 | ModelOP | Score44.90% | Percentile33.33% | Participants10 | EvidenceC | Evaluated |
| Rank08 | ModelOP | Score31.60% | Percentile22.22% | Participants10 | EvidenceC | Evaluated |
| Rank09 | ModelOP | Score18.20% | Percentile11.11% | Participants10 | EvidenceC | Evaluated |
| Rank10 | ModelOP | Score6.20% | Percentile0.00% | Participants10 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What Aider-Polyglot Edit measures and how its scores work.
A benchmark for evaluating models on code-editing tasks using 225 Exercism programming problems selected because 3 or fewer of 7 top coding models solved them.
It measures solution correctness and proper edit format usage, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. 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 79.70%.
The current leaders are DeepSeek-V3 (79.70%), Gemini 2.5 Pro (72.70%), and o3-mini (60.40%).
GPT-4.1 nano has the lowest matched official input price at $0.10 input / $0.40 output per 1M tokens.
The fastest matched records are GPT-4.1 mini (467.39 tok/s via OpenAI), GPT-4.1 nano (138.14 tok/s via OpenAI), and GPT-4o (132.00 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures solution correctness and proper edit format usage, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
10 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis aider-polyglot edit AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
DeepSeek-V3 currently leads Aider-Polyglot Edit with 79.70%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.