Code editing across 8 programming languages: Aider Polyglot
Real-world code editing benchmark measuring a model's ability to apply changes to existing codebases across 8 programming languages using Exercism platform exercises.
Models ranked
33
tracked on this benchmark
Score band
88.0 – 3.6
best → lowest tracked
Snapshot trend
-3.50
May 29 → Jul 1 · 1 models
Leaderboard
Tracked models ranked by % Exercises Completed (higher is better).
Notes: Confidence: high. DAT-3780 weekly best-of refresh: uses the highest GPT-5 thinking-level score reported by aider.chat.
Notes: Confidence: high. DAT-4172 May 12 /best/ refresh; aider leaderboard listed claude-opus-4-20250514 32k thinking at 72.0%.
How to read this benchmark
This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
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Last reviewed: Apr 15, 2026